Vector Database

01May

Public Sector Einstein 1 for Service

Salesforce, a prominent provider of cloud-based software solutions, has unveiled the introduction of Public Sector Einstein 1 for Service, a specialized software platform tailored explicitly for government employees. Salesforce is targeting governmental customer service improvements with the launch of the Public Sector Einstein 1 for Service. This latest offering, built on Salesforce’s Einstein 1 platform, integrates a variety of artificial intelligence-driven capabilities aimed at streamlining administrative tasks within the public sector. Public Sector Einstein 1 for Service. Built on Salesforce’s Einstein 1 platform, the offering is designed to leverage data and automation to improve worker efficiency, reduce or eliminate repetitive tasks, and improve the ability of workers to interact with systems, data, and the people they serve. Public Sector Einstein 1 for Service presents a suite of AI-powered features crafted to enhance efficiency and productivity for government entities. These encompass Caseworker Narrative Generation, utilizing generative AI to synthesize data summaries; Service Cloud Voice, enabling real-time transcription of conversations; and Einstein Activity Capture for Public Sector, facilitating documentation of case interactions through natural language processing. Additionally, the platform incorporates Data Cloud for Public Sector and Interaction Notes for Public Sector, providing comprehensive note-taking functionalities. Salesforce’s Executive Vice President and General Manager for the Public Sector, Nasi Jazayeri, underscored the significance of harnessing trusted AI to enhance operational effectiveness, data management, and service delivery for government agencies, empowering employees to better serve constituents. Having previously provided tools for other FEDramp-compliant products – including Field Service and Security Center – Salesforce’s newest solution utilizes trusted conversational and generative AI (GenAI) to improve agent efficiency. The solution also promises public sector organizations the ability to swiftly generate case reports, record real-time call transcriptions, and document and format case interactions—all through a single unified solution. Another key aspect of the tool is the inclusion of Salesforce’s Data Cloud system, which allows users to consolidate data from various sources – including benefits, education, and healthcare – into a standardized data model. Public Sector Einstein 1 for Service also includes Data Cloud, which is designed to capture, connect, and harmonize an organization’s entire corpus of data into a common data model. This can be used to create unified constituent profiles that serve as a single source of truth for the organization, enabling the organization to personalize outreach and interactions. A new feature being offered is Interaction Notes for Public Sector, which allows caseworkers to take detailed notes of their meetings and conversations with constituents or other case participants, specify the confidentiality level of the notes, add action items or next steps, and then search for and filter summaries to find notes from previous interactions, all in one place. This feature takes a common practice deployed at many public sector agencies and helps to organize information that can often be lost when managed through manual processes. Of course this also brings in the Salesforce Vector Database. In doing so, public service organizations are able to create specific profiles for their constituents and personalize their customer service offerings accordingly. If you have contemplated adding Salesforce Nonprofit Cloud, check out this new offering from Tectonic – Salesforce Imploementation Solutions. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

May 1, 2024in Einstein 1 Platform, Salesforce, Salesforce Implementation Services, Salesforce Public Sector Solutions

25Apr

Salesforce Spiff Announced

Salesforce unveiled Salesforce Spiff yesterday, introducing incentive compensation management directly into the world’s leading AI CRM to automate commissions and boost seller motivation. With this enhancement, Sales Cloud now offers sellers and sales leaders a comprehensive growth platform covering the entire journey from pipeline development to paycheck delivery. Recently integrated into Salesforce’s ecosystem through acquisition, Spiff empowers organizations to increase revenue by aiding sales leaders in managing intricate incentive compensation plans and understanding the multiple factors influencing revenue performance. This product boasts an intuitive user interface, real-time visibility, transparency into critical financial data, comprehensive analytics and reporting capabilities, and seamless integration with other Salesforce applications. Significance of Salesforce Spiff: Many organizations struggle with setting accurate quotas for sales compensation programs, with 64% citing this as a major challenge. Incentive-based pay is a fundamental component of total compensation, with 90% of top-performing companies employing incentive programs to reward sales associates. Overcoming these hurdles is vital for optimizing sales performance and achieving organizational objectives. Additionally, incentive packages often vary by level and business objective, posing manual management challenges without compensation management technology. Innovative Features of Salesforce Spiff: Salesforce Spiff Commission Estimator: Sales reps can view estimated commissions and align them with business objectives while creating customer quotes in Sales Cloud. Salesforce Spiff Rep Dashboards & Mobile App: Real-time dashboards allow sales reps to track commission trajectories seamlessly within their workflows. Time-saving Tools for Commission Administrators: Salesforce Spiff Commission Designer: Employ a low-code commission builder to visualize the potential impact of various plan amendments. Salesforce Spiff Assistant: Leverage a conversational AI assistant to gain quick insights into sales plans, rules, and calculations. This tool provides logic, error, and filter explanations as well as formula optimization in natural language, simplifying plan building and management. Salesforce’s Perspective on Spiff: “Sales leaders understand the critical role of compensation in driving sales rep behavior. The challenge lies in aligning compensation plans with desired outcomes while navigating data across fragmented point solutions,” stated Ketan Karkhanis, EVP & GM of Sales Cloud. “Spiff bridges the gap between what sellers desire—transparent compensation—and what sales leaders seek—compensation planning integrated into CRM that aligns behaviors with strategic outcomes.” “One of our biggest challenges was engaging our sales reps with their compensation plans to motivate them to achieve their goals. Spiff has provided us with a platform to showcase our commitment to our culture and employees. Spiff has truly transformed our commission program,” Lindsey Sanford, Senior Director of Sales and Marketing at RadNet Availability: Salesforce Spiff will be accessible as an add-on for Sales Cloud customers in the upcoming months. Non-Salesforce customers can also purchase the product by visiting Salesforce.com/salesforcespiff. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

April 25, 2024in Salesforce, Salesforce Sales Cloud

21Apr

Generative AI Prompts with Retrieval Augmented Generation

By now, you’ve likely experimented with generative AI language models (LLMs) such as OpenAI’s ChatGPT or Google’s Gemini to aid in composing emails or crafting social media content. Yet, achieving optimal results can be challenging—particularly if you haven’t mastered the art and science of formulating effective prompts. Generative AI Prompts with Retrieval Augmented Generation. The effectiveness of an AI model hinges on its training data. To excel, it requires precise context and substantial factual information, rather than generic details. This is where Retrieval Augmented Generation (RAG) comes into play, enabling you to seamlessly integrate your most current and pertinent proprietary data directly into your LLM prompt. Here’s a closer look at how RAG operates and the benefits it can offer your business. Generative AI Prompts with Retrieval Augmented Generation Why RAG Matters: An AI model’s efficacy is determined by the quality of its training data. For optimal performance, it needs specific context and substantial factual information, not just generic data. An off-the-shelf LLM lacks the real-time updates and trustworthy access to proprietary data essential for precise responses. RAG addresses this gap by embedding up-to-date and pertinent proprietary data directly into LLM prompts, enhancing response accuracy. How RAG Works: RAG leverages powerful semantic search technologies within Salesforce to retrieve relevant information from internal data sources like emails, documents, and customer records. This retrieved data is then fed into a generative AI model (such as CodeT5 or Einstein Language), which uses its language understanding capabilities to craft a tailored response based on the retrieved facts and the specific context of the user’s query or task. Case Study: Algo Communications In 2023, Canada-based Algo Communications faced the challenge of rapidly onboarding customer service representatives (CSRs) to support its growth. Seeking a robust solution, the company turned to generative AI, adopting an LLM enhanced with RAG for training CSRs to accurately respond to complex customer inquiries. Algo integrated extensive unstructured data, including chat logs and email history, into its vector database, enhancing the effectiveness of RAG. Within just two months of adopting RAG, Algo’s CSRs exhibited greater confidence and efficiency in addressing inquiries, resulting in a 67% faster resolution of cases. Key Benefits of RAG for Algo Communications: Efficiency Improvement: RAG enabled CSRs to complete cases more quickly, allowing them to address new inquiries at an accelerated pace. Enhanced Onboarding: RAG reduced onboarding time by half, facilitating Algo’s rapid growth trajectory. Brand Consistency: RAG empowered CSRs to maintain the company’s brand identity and ethos while providing AI-assisted responses. Human-Centric Customer Interactions: RAG freed up CSRs to focus on adding a human touch to customer interactions, improving overall service quality and customer satisfaction. Retrieval Augmented Generation (RAG) enhances the capabilities of generative AI models by integrating current and relevant proprietary data directly into LLM prompts, resulting in more accurate and tailored responses. This technology not only improves efficiency and onboarding but also enables organizations to maintain brand consistency and deliver exceptional customer experiences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

April 21, 2024in Generative AI, Salesforce

07Apr

LLM Knowledge Test

Large Language Models. How much do you know about them? Take the LLM Knowledge Test to find out. Question 1Do you need to have a vector store for all your text-based LLM use cases? A. Yes B. No Correct Answer: B ExplanationA vector store is used to store the vector representation of a word or sentence. These vector representations capture the semantic meaning of the words or sentences and are used in various NLP tasks. However, not all text-based LLM use cases require a vector store. Some tasks, such as summarization, sentiment analysis, and translation, do not need context augmentation. Here is why: Question 2Which technique helps mitigate bias in prompt-based learning? A. Fine-tuning B. Data augmentation C. Prompt calibration D. Gradient clipping Correct Answer: C ExplanationPrompt calibration involves adjusting prompts to minimize bias in the generated outputs. Fine-tuning modifies the model itself, while data augmentation expands the training data. Gradient clipping prevents exploding gradients during training. Question 3Which of the following is NOT a technique specifically used for aligning Large Language Models (LLMs) with human values and preferences? A. RLHF B. Direct Preference Optimization C. Data Augmentation Correct Answer: C ExplanationData Augmentation is a general machine learning technique that involves expanding the training data with variations or modifications of existing data. While it can indirectly impact LLM alignment by influencing the model’s learning patterns, it’s not specifically designed for human value alignment. Incorrect Options: A) Reinforcement Learning from Human Feedback (RLHF) is a technique where human feedback is used to refine the LLM’s reward function, guiding it towards generating outputs that align with human preferences. B) Direct Preference Optimization (DPO) is another technique that directly compares different LLM outputs based on human preferences to guide the learning process. Question 4In Reinforcement Learning from Human Feedback (RLHF), what describes “reward hacking”? A. Optimizes for desired behavior B. Exploits reward function Correct Answer: B ExplanationReward hacking refers to a situation in RLHF where the agent discovers unintended loopholes or biases in the reward function to achieve high rewards without actually following the desired behavior. The agent essentially “games the system” to maximize its reward metric. Why Option A is Incorrect:While optimizing for the desired behavior is the intended outcome of RLHF, it doesn’t represent reward hacking. Option A describes a successful training process. In reward hacking, the agent deviates from the desired behavior and finds an unintended way to maximize the reward. Question 5Fine-tuning GenAI model for a task (e.g., Creative writing), which factor significantly impacts the model’s ability to adapt to the target task? A. Size of fine-tuning dataset B. Pre-trained model architecture Correct Answer: B ExplanationThe architecture of the pre-trained model acts as the foundation for fine-tuning. A complex and versatile architecture like those used in large models (e.g., GPT-3) allows for greater adaptation to diverse tasks. The size of the fine-tuning dataset plays a role, but it’s secondary. A well-architected pre-trained model can learn from a relatively small dataset and generalize effectively to the target task. Why A is Incorrect:While the size of the fine-tuning dataset can enhance performance, it’s not the most crucial factor. Even a massive dataset cannot compensate for limitations in the pre-trained model’s architecture. A well-designed pre-trained model can extract relevant patterns from a smaller dataset and outperform a less sophisticated model with a larger dataset. Question 6What does the self-attention mechanism in transformer architecture allow the model to do? A. Weigh word importance B. Predict next word C. Automatic summarization Correct Answer: A ExplanationThe self-attention mechanism in transformers acts as a spotlight, illuminating the relative importance of words within a sentence. In essence, self-attention allows transformers to dynamically adjust the focus based on the current word being processed. Words with higher similarity scores contribute more significantly, leading to a richer understanding of word importance and sentence structure. This empowers transformers for various NLP tasks that heavily rely on context-aware analysis. Incorrect Options: Question 7What is one advantage of using subword algorithms like BPE or WordPiece in Large Language Models (LLMs)? A. Limit vocabulary size B. Reduce amount of training data C. Make computationally efficient Correct Answer: A ExplanationLLMs deal with massive amounts of text, leading to a very large vocabulary if you consider every single word. Subword algorithms like Byte Pair Encoding (BPE) and WordPiece break down words into smaller meaningful units (subwords) which are then used as the vocabulary. This significantly reduces the vocabulary size while still capturing the meaning of most words, making the model more efficient to train and use. Incorrect Answer Explanations: Question 8Compared to Softmax, how does Adaptive Softmax speed up large language models? A. Sparse word reps B. Zipf’s law exploit C. Pre-trained embedding Correct Answer: B ExplanationStandard Softmax struggles with vast vocabularies, requiring expensive calculations for every word. Imagine a large language model predicting the next word in a sentence. Softmax multiplies massive matrices for each word in the vocabulary, leading to billions of operations! Adaptive Softmax leverages Zipf’s law (common words are frequent, rare words are infrequent) to group words by frequency. Frequent words get precise calculations in smaller groups, while rare words are grouped together for more efficient computations. This significantly reduces the cost of training large language models. Incorrect Answer Explanations: Question 9Which configuration parameter for inference can be adjusted to either increase or decrease randomness within the model output layer? A. Max new tokens B. Top-k sampling C. Temperature Correct Answer: C ExplanationDuring text generation, large language models (LLMs) rely on a softmax layer to assign probabilities to potential next words. Temperature acts as a key parameter influencing the randomness of these probability distributions. Why other options are incorrect: Question 10What transformer model uses masking & bi-directional context for masked token prediction? A. Autoencoder B. Autoregressive C. Sequence-to-sequence Correct Answer: A ExplanationAutoencoder models are pre-trained using masked language modeling. They use randomly masked tokens in the input sequence, and the pretraining objective is to predict the masked tokens to reconstruct the original sentence. Question 11What technique allows you to scale model

April 7, 2024in Generative AI

30Dec

January ’24 Einstein Data Cloud Updates

Utilize Generative AI to Target Audiences Effectively Harness the power of generative AI with Einstein Segment Creation in Data Cloud to create precise audience segments. Describe your target audience, and Einstein Segment Creation swiftly produces a segment using trusted customer data available in Data Cloud. This segment can be easily edited and fine-tuned as necessary. Jan ’24 Einstein Data Cloud Updates. Where: This enhancement is applicable to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Einstein generative AI is accessible in Lightning Experience. When: This functionality is rolling out gradually, starting in Spring ’24. How: In Data Cloud, create a new segment and choose Einstein Segment Creation. In the Einstein panel, input a description of your segment using simple text, review the draft, and make adjustments as needed. Gain Insights into Segment Performance with Segment Intelligence Analyze segment data efficiently with Segment Intelligence, an in-platform intelligence tool for Data Cloud for Marketing. Offering a straightforward setup process, out-of-the-box data connectors, and pre-built visualizations, Segment Intelligence aids in optimizing segments and activations across various channels, including Marketing Cloud Engagement, Google Ads, Meta Ads, and Commerce Cloud. Where: This update applies to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Utilizing Segment Intelligence requires a Data Cloud Starter license. When: For details regarding timing and eligibility, contact your Salesforce account executive. How: To configure Segment Intelligence, navigate to Salesforce Setup. To view Segment Intelligence dashboards, go to Data Cloud and select the Segment Intelligence tab. Activate Audiences on Google DV360 and LinkedIn Effortlessly activate audiences on Google DV360 and LinkedIn as native activation destinations in Data Cloud. Directly use segments for targeted advertising campaigns and insights reporting. Where: This change is applicable to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Requires an Ad Audiences license. When: This functionality is available starting in March 2024. Enhance Identity Resolution with More Frequent Ruleset Processing Experience more timely ruleset processing as rulesets now run automatically whenever your data changes. This improvement eliminates the need to wait for a daily ruleset run, ensuring efficient and cost-effective processing. Where: This update applies to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Refine Identity Resolution Match Rules with Fuzzy Matching Extend the use of fuzzy matching to more fields, allowing fuzzy matching on any text field in your identity resolution match rules. Up to two fuzzy match fields, other than first name, can be used in a match rule, with a total of six fuzzy match fields in any ruleset. Enhance match rules by updating to the “Fuzzy Precision – High” method for fields like last name, city, and account. Where: This enhancement applies to Data Cloud in Developer, Enterprise, Performance, and Unlimited editions. Salesforce Einstein’s AI Capabilities Salesforce Einstein stands out as a comprehensive AI solution for CRM. Notable features include being data-ready, eliminating the need for data preparation or model management. Simply input data into Salesforce, and Einstein seamlessly operates. Additionally, Salesforce introduces the Data Cloud, formerly known as Genie, as a significant AI-powered product. This platform, combining Data Cloud and AI in Einstein 1, empowers users to manage unstructured data efficiently. The introduction of the Data Cloud Vector Database allows for the storage and retrieval of unstructured data, enabling Einstein Copilot to search and interpret vast amounts of information. Salesforce also unveils Einstein Copilot Search, currently in closed beta, enhancing AI search capabilities to respond to complex queries from users. Jan ’24 Einstein Data Cloud Updates This groundbreaking offering addresses the challenge of managing unstructured data, a substantial portion of business data, and complements it with the capability to use familiar automation tools such as Flow and Apex to monitor and trigger workflows based on changes in this data. Overall, Salesforce aims to revolutionize how organizations handle unstructured data with these innovative additions to the Data Cloud. Like2 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more

December 30, 2023in Einstein 1 Platform, Einstein Insights, Salesforce Data Cloud, Salesforce Einstein

14Dec

Salesforce Enhances Einstein 1 Platform with New Vector Database and AI Capabilities

Salesforce (NYSE: CRM) has announced major updates to its Einstein 1 Platform, introducing the Data Cloud Vector Database and Einstein Copilot Search. These new features aim to power AI, analytics, and automation by integrating business data with large language models (LLMs) across the Einstein 1 Platform. Salesforce Enhances Einstein 1 Platform with New Vector Database and AI Capabilities. Unifying Business Data for Enhanced AI The Data Cloud Vector Database will unify all business data, including unstructured data like PDFs, emails, and transcripts, with CRM data. This will enable accurate and relevant AI prompts and Einstein Copilot, eliminating the need for expensive and complex fine-tuning of LLMs. Built into the Einstein 1 Platform, the Data Cloud Vector Database allows all business applications to harness unstructured data through workflows, analytics, and automation. This enhances decision-making and customer insights across Salesforce CRM applications. Introducing Einstein Copilot Search Einstein Copilot Search will provide advanced AI search capabilities, delivering precise answers from the Data Cloud in a conversational AI experience. This feature aims to boost productivity for all business users by interpreting and responding to complex queries with real-time data from various sources. Key Features and Benefits Salesforce Enhances Einstein 1 Platform with New Vector Database and AI Capabilities Data Cloud Vector Database Einstein Copilot Search Addressing the Data Challenge With 90% of enterprise data existing in unstructured formats, accessing and leveraging this data for business applications and AI models has been challenging. As Forrester predicts, the volume of unstructured data managed by enterprises will double by 2024. Salesforce’s new capabilities address this by enabling businesses to effectively harness their data, driving AI innovation and improved customer experiences. Salesforce’s Vision Rahul Auradkar, EVP and GM of Unified Data Services & Einstein, stated, “The Data Cloud Vector Database transforms all business data into valuable insights. This advancement, coupled with the power of LLMs, fosters a data-driven ecosystem where AI, CRM, automation, Einstein Copilot, and analytics turn data into actionable intelligence and drive innovation.” Practical Applications Customer Success Story Shohreh Abedi, EVP at AAA – The Auto Club Group, highlighted the impact: “With Salesforce automation and AI, we’ve reduced response time for roadside events by 10% and manual service cases by 30%. Salesforce AI helps us deliver faster support and increased productivity.” Availability Salesforce Enhances Einstein 1 Platform with New Vector Database and AI Capabilities Salesforce’s new Data Cloud Vector Database and Einstein Copilot Search promise to revolutionize how businesses utilize their data, driving AI-powered innovation and improved customer experiences. Salesforce Enhances Einstein 1 Platform with New Vector Database and AI Capabilities Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

December 14, 2023in Einstein 1 Platform, Salesforce

13Dec

Retrieval Augmented Generation Techniques

A comprehensive study has been conducted on advanced retrieval augmented generation techniques and algorithms, systematically organizing various approaches. This insight includes a collection of links referencing various implementations and studies mentioned in the author’s knowledge base. If you’re familiar with the RAG concept, skip to the Advanced RAG section. Retrieval Augmented Generation, known as RAG, equips Large Language Models (LLMs) with retrieved information from a data source to ground their generated answers. Essentially, RAG combines Search with LLM prompting, where the model is asked to answer a query provided with information retrieved by a search algorithm as context. Both the query and the retrieved context are injected into the prompt sent to the LLM. RAG emerged as the most popular architecture for LLM-based systems in 2023, with numerous products built almost exclusively on RAG. These range from Question Answering services that combine web search engines with LLMs to hundreds of apps allowing users to interact with their data. Even the vector search domain experienced a surge in interest, despite embedding-based search engines being developed as early as 2019. Vector database startups such as Chroma, Weavaite.io, and Pinecone have leveraged existing open-source search indices, mainly Faiss and Nmslib, and added extra storage for input texts and other tooling. Two prominent open-source libraries for LLM-based pipelines and applications are LangChain and LlamaIndex, both founded within a month of each other in October and November 2022, respectively. These were inspired by the launch of ChatGPT and gained massive adoption in 2023. The purpose of this Tectonic insight is to systemize key advanced RAG techniques with references to their implementations, mostly in LlamaIndex, to facilitate other developers’ exploration of the technology. The problem addressed is that most tutorials focus on individual techniques, explaining in detail how to implement them, rather than providing an overview of the available tools. Naive RAG The starting point of the RAG pipeline described in this article is a corpus of text documents. The process begins with splitting the texts into chunks, followed by embedding these chunks into vectors using a Transformer Encoder model. These vectors are then indexed, and a prompt is created for an LLM to answer the user’s query given the context retrieved during the search step. In runtime, the user’s query is vectorized with the same Encoder model, and a search is executed against the index. The top-k results are retrieved, corresponding text chunks are fetched from the database, and they are fed into the LLM prompt as context. An overview of advanced RAG techniques, illustrated with core steps and algorithms. 1.1 Chunking Texts are split into chunks of a certain size without losing their meaning. Various text splitter implementations capable of this task exist. 1.2 Vectorization A model is chosen to embed the chunks, with options including search-optimized models like bge-large or E5 embeddings family. 2.1 Vector Store Index Various indices are supported, including flat indices and vector indices like Faiss, Nmslib, or Annoy. 2.2 Hierarchical Indices Efficient search within large databases is facilitated by creating two indices: one composed of summaries and another composed of document chunks. 2.3 Hypothetical Questions and HyDE An alternative approach involves asking an LLM to generate a question for each chunk, embedding these questions in vectors, and performing query search against this index of question vectors. 2.4 Context Enrichment Smaller chunks are retrieved for better search quality, with surrounding context added for the LLM to reason upon. 2.4.1 Sentence Window Retrieval Each sentence in a document is embedded separately to provide accurate search results. 2.4.2 Auto-merging Retriever Documents are split into smaller child chunks referring to larger parent chunks to enhance context retrieval. 2.5 Fusion Retrieval or Hybrid Search Keyword-based old school search algorithms are combined with modern semantic or vector search to improve retrieval results. Encoder and LLM Fine-tuning Fine-tuning of Transformer Encoders or LLMs can further enhance the RAG pipeline’s performance, improving context retrieval quality or answer relevance. Evaluation Various frameworks exist for evaluating RAG systems, with metrics focusing on retrieved context relevance, answer groundedness, and overall answer relevance. The next big thing about building a nice RAG system that can work more than once for a single query is the chat logic, taking into account the dialogue context, same as in the classic chat bots in the pre-LLM era.This is needed to support follow up questions, anaphora, or arbitrary user commands relating to the previous dialogue context. It is solved by query compression technique, taking chat context into account along with the user query. Query routing is the step of LLM-powered decision making upon what to do next given the user query — the options usually are to summarise, to perform search against some data index or to try a number of different routes and then to synthesise their output in a single answer. Query routers are also used to select an index, or, broader, data store, where to send user query — either you have multiple sources of data, for example, a classic vector store and a graph database or a relational DB, or you have an hierarchy of indices — for a multi-document storage a pretty classic case would be an index of summaries and another index of document chunks vectors for example. This insight aims to provide an overview of core algorithmic approaches to RAG, offering insights into techniques and technologies developed in 2023. It emphasizes the importance of speed in RAG systems and suggests potential future directions, including exploration of web search-based RAG and advancements in agentic architectures. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the

December 13, 2023in Salesforce Einstein

22Nov

Salesforce Einstein 1 Platform

Salesforce unveils the groundbreaking Einstein 1 Platform, a transformative force in enterprise AI designed to enhance productivity and cultivate trusted customer experiences by seamlessly integrating data, AI, and CRM. This advanced platform meets the demands of a new AI era, adeptly managing extensive disconnected data, offering flexibility in AI model selection, and seamlessly integrating with workflow processes while prioritizing customer trust. Salesforce Einstein 1 Platform is a game changer from Salesforce. What is the Salesforce Einstein 1 platform? Einstein 1 has a mixture of artificial intelligence tools on the platform, and it kind of mirrors the way the core Salesforce platform is built, standardized and custom. We have out of the box AI features such as sales email generation in Sales Cloud, and service replies in Service Cloud. The Einstein 1 Platform consolidates data, AI, CRM, development, and security into a unified, comprehensive platform, empowering IT professionals, administrators, and developers with an extensible AI platform for rapid app development and automation. Streamlining change and release management, the DevOps Center allows centralized oversight of project work at every stage of the application lifecycle management process, ensuring secure data testing and AI app deployment. Salesforce customizes security and privacy add-on solutions, including data monitoring and masking, backup implementation, and compliance with evolving privacy and encryption regulations. Grounded in the Einstein 1 Platform, Salesforce AI delivers trusted and customizable experiences by leveraging customer data to create predictive and generative interactions tailored to diverse business needs. What are the Einstein platform products? Commerce Cloud Einstein is a generative AI tool that can be used to provide personalized commerce experiences throughout the entire buyer’s journey. It can be used to generate auto-generated recommendations, content, and communications that are based on real-time data from the Data Cloud. Einstein 1 serves as a comprehensive solution for organizations seeking a unified 360-degree view of their customers, integrating Silverline expertise to maximize AI potential and scalability. The introduction of Einstein 1 Data Cloud addresses data integration challenges, enabling users to connect any data source for a unified customer profile enriched with AI, automation, and analytics. Salesforce Data Cloud unifies and harmonizes customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data to create a single view of the customer. Einstein 1 Data Cloud is natively integrated with the Einstein 1 Platform and allows companies to unlock siloed data and scale in entirely new ways, including: Supporting thousands of metadata-enabled objects per customer, the platform ensures scalability, while re-engineering Marketing Cloud and Commerce Cloud onto the Einstein 1 Platform enables seamless incorporation of massive data volumes. Salesforce offers Data Cloud at no cost for Enterprise Edition or above customers, underscoring its commitment to supporting businesses at various stages of maturity. Einstein Copilot Search and the Data Cloud Vector Database further enhance Einstein 1 capabilities, providing improved AI search and unifying structured and unstructured data for informed workflows and automation. Einstein 1 introduces generative AI-powered conversational assistants, operating within the secure Einstein Trust Layer to enhance productivity while ensuring data privacy. Businesses are encouraged to embrace Einstein 1 as a strategic move toward becoming AI-centric, leveraging its unified data approach to effectively train AI models for informed decision-making. Salesforce’s Einstein 1 Platform introduces the Data Cloud Vector Database, seamlessly unifying structured and unstructured business data to enhance AI prompts and streamline workflows. Generative AI impacts businesses differently, augmenting processes to improve efficiency and productivity across sales, service, and field service teams. Einstein 1 Platform addresses challenges of fragmented customer data, offering a unified view for effective AI model training and decision-making. Salesforce’s continuous evolution ensures businesses have access to cutting-edge AI technologies, positioning Einstein 1 as a crucial tool for staying ahead in the AI-centric landscape. Ready to explore Data Cloud for Einstein 1? Limited access is available for $0, offering businesses an exclusive opportunity to leverage this transformative solution. Salesforce’s Einstein 1 Platform introduces advancements in AI search capabilities and unification of structured and unstructured business data, empowering informed workflows and automation. Einstein GPT expands conversational AI across Marketing and Commerce clouds, with the Data Cloud Vector Database playing a pivotal role in unifying data for Einstein 1 users. Einstein now has a generative AI-powered conversational AI assistant that includes Einstein Copilot and Einstein Copilot Studio. These two capabilities operate within the Einstein Trust Layer – a secure AI architecture built natively into the Einstein 1 Platform that allows you to leverage generative AI while preserving data privacy and security standards. Einstein Copilot is an out-of-the-box conversational AI assistant built into the user experience of every Salesforce application. Einstein Copilot drives productivity by assisting users within their flow of work, enabling them to ask questions in natural language and receive relevant and trustworthy answers grounded in secure proprietary company data from Data Cloud. Data Cloud Vector Database simplifies data integration, enhancing AI prompts without costly updates to specific business models. Data Cloud, integrated with the Einstein Trust Layer, provides secure data access and visualization, enabling businesses to fully harness generative AI. Einstein 1, with Data Cloud, offers a solution for organizations seeking comprehensive customer insights, guided by Silverline expertise for AI maximization. Salesforce’s Einstein 1 Platform securely integrates data, connecting various products to empower customer-centric businesses with AI-driven applications. Data Cloud for Einstein 1 supports AI assistants and enhances customer experiences, driving productivity and reducing operational costs. Einstein 1’s impact is evident in increased productivity and enhanced customer experiences, with ongoing evolution ensuring businesses stay at the forefront of AI technology. Generative AI augments existing processes, particularly in sales, service, and customer support, with Einstein 1 providing tools for streamlined operations. Salesforce’s Einstein 1 Platform introduces AI search enhancements and unified data capabilities, empowering businesses with informed decision-making and automation. Ready to embrace AI-driven productivity? Explore Data Cloud for Einstein 1 and revolutionize your business operations today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce

November 22, 2023in Einstein 1 Platform, Salesforce, Salesforce Einstein

04Nov

Fine Tune Your Large Language Model

Revamping Your LLM? There’s a Superior Approach to Fine Tune Your Large Language Model. The next evolution in AI fine-tuning might transcend fine-tuning altogether. Vector databases present an efficient means to access and analyze all your business data. Have you ever received redundant emails promoting a product you’ve already purchased or encountered repeated questions in different service interactions? Large language models (LLMs), like OpenAI’s ChatGPT and Google’s Bard, aim to alleviate such issues by enhancing information-sharing and personalization within your company’s operations. However, off-the-shelf LLMs, built on generic internet data, lack access to your proprietary data, limiting the nuanced customer experience. Additionally, these models might not incorporate the latest information—ChatGPT, for instance, only extends up to January 2022. To customize off-the-shelf LLMs for your company, fine-tuning requires integrating your proprietary data, but this process is costly, time-consuming, and may raise trust concerns. A superior alternative is a vector database, described as “a new kind of database for the AI era.” This database offers the benefits of fine-tuning while addressing privacy concerns, promoting data unification, and saving time and resources. Fine-tuning involves training an LLM for specific tasks, such as analyzing customer sentiment or summarizing a patient’s health history. However, it is resource-intensive and fails to resolve the fundamental issue of fragmented data across your organization. A vector database, organized around vectors that describe different types of data, can seamlessly integrate with an LLM or the prompt. By storing and organizing data with an emphasis on vectors, this database streamlines access to relevant information, eliminating the need for fine-tuning and unifying enterprise data with your CRM. This is pivotal for the accuracy, completeness, and efficiency of AI outputs. Unstructured data, comprising 90% of corporate data, poses a challenge for LLMs due to its varied formats. A vector database resolves this by allowing AI to process unstructured and structured data, delivering enhanced business value and ROI. Ultimately, a company’s proprietary data serves as the cornerstone for constructing an enterprise LLM. A vector database ensures seamless storage and processing of this data, facilitating better decision-making across all business applications. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

November 4, 2023in Salesforce Einstein

26Oct

RAG – Retrieval Augmented Generation in Artificial Intelligence

Salesforce has introduced advanced capabilities for unstructured data in Data Cloud and Einstein Copilot Search. By leveraging semantic search and prompts in Einstein Copilot, Large Language Models (LLMs) now generate more accurate, up-to-date, and transparent responses, ensuring the security of company data through the Einstein Trust Layer. Retrieval Augmented Generation in Artificial Intelligence has taken Salesforce’s Einstein and Data Cloud to new heights. These features are supported by the AI framework called Retrieval Augmented Generation (RAG), allowing companies to enhance trust and relevance in generative AI using both structured and unstructured proprietary data. RAG Defined: RAG assists companies in retrieving and utilizing their data, regardless of its location, to achieve superior AI outcomes. The RAG pattern coordinates queries and responses between a search engine and an LLM, specifically working on unstructured data such as emails, call transcripts, and knowledge articles. How RAG Works: Salesforce’s Implementation of RAG: RAG begins with Salesforce Data Cloud, expanding to support storage of unstructured data like PDFs and emails. A new unstructured data pipeline enables teams to select and utilize unstructured data across the Einstein 1 Platform. The Data Cloud Vector Database combines structured and unstructured data, facilitating efficient processing. RAG in Action with Einstein Copilot Search: RAG for Enterprise Use: RAG aids in processing internal documents securely. Its four-step process involves ingestion, natural language query, augmentation, and response generation. RAG prevents arbitrary answers, known as “hallucinations,” and ensures relevant, accurate responses. Applications of RAG: RAG offers a pragmatic and effective approach to using LLMs in the enterprise, combining internal or external knowledge bases to create a range of assistants that enhance employee and customer interactions. Retrieval-augmented generation (RAG) is an AI technique for improving the quality of LLM-generated responses by including trusted sources of knowledge, outside of the original training set, to improve the accuracy of the LLM’s output. Implementing RAG in an LLM-based question answering system has benefits: 1) assurance that an LLM has access to the most current, reliable facts, 2) reduce hallucinations rates, and 3) provide source attribution to increase user trust in the output. Retrieval Augmented Generation in Artificial Intelligence Content updated July 2024. Like2 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 26, 2023in Einstein 1 Platform, Generative AI, Salesforce, Salesforce Data Cloud

08Sep

Salesforce Data Cloud Evolution

Data Cloud stands as the fastest-growing organically built product in Salesforce’s history, signifying a significant milestone in solving the enduring data problem within Customer Relationship Management (CRM). Salesforce Data Cloud Evolution since its beginnings is an interesting story. With an average of 928 systems per company, identity resolution becomes challenging, especially when managing more than one system. Salesforce’s expansion into AI-powered CRM emphasizes the synergy between AI and data, recognizing that AI’s optimal functionality requires robust data support. Data Cloud acts as the foundation accelerating connectivity across different ‘clouds’ within the Salesforce platform. While it’s available for purchase, even Salesforce customers without licensed Data Cloud still benefit from its foundational advantages, with increased strength when utilized as a personalization and data unification platform. The history of Data Cloud reflects its evolution through various iterations, from Customer 360 Audiences to Salesforce Genie, ultimately settling as Data Cloud in 2023. This journey marked significant developments, expanding from a marketer’s tool to catering for sales, service, and diverse use cases across the Salesforce platform. Data harmonization with Data Cloud simplifies the complex process, requiring fewer efforts compared to traditional methods. It comes pre-wired to Salesforce objects, reducing the need for extensive data modeling and integration steps. The technical capability map showcases a comprehensive integration of various technologies, making Data Cloud versatile and adaptable. Data Cloud’s differentiators include being pre-wired to Salesforce objects, industry-specific data models, prompt engineering capabilities, and the inclusion of the Einstein Trust Layer, addressing concerns related to generative AI adoption. Looking ahead, Data Cloud continues to evolve with constant innovation and features in Salesforce’s major releases. The introduction of Data Cloud for Industries, starting with Health Cloud, signifies ongoing enhancements to cater to industry-specific needs. Closing the skills gap is crucial for effective Data Cloud implementation, requiring a blend of developer skills, data management expertise, business analyst skills, and proficiency in prompt engineering. Salesforce envisions Data Cloud, combined with CRM and AI, as the next generation of customer relationship management, emphasizing the importance of sound data and skillful implementation. Data Cloud represents the ‘Holy Grail of CRM,’ offering a solution to the long-standing data challenges in CRM. However, its success as an investment depends on the organization’s readiness to demonstrate return on investment (ROI) through solid use cases, ensuring unified customer profiles and reaping the rewards of this transformative technology. FAQ When did Salesforce introduce data cloud? Customer 360 Audiences: Salesforce’s initial CDP offering, launched in 2020. Salesforce CDP: The name changed in 2021 to align with how the blooming CDP market was referring to this technology. Does Salesforce data cloud compete with Snowflake? They offer distinct capabilities and cater to diverse business needs. Salesforce Data Cloud specializes in data enrichment, personalization, and real-time updates, while Snowflake boasts scalable data warehousing and powerful analytics capabilities. What is the data cloud in Salesforce? Deeply integrated into the Einstein 1 Platform, Data Cloud makes all your data natively available across all Salesforce applications — Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, Tableau, and MuleSoft — to power automation and business processes and inform AI. Is Salesforce Genie now data cloud? Announced at Dreamforce ’22, Salesforce Genie was declared the greatest Salesforce innovation in the company’s history. Now known as Data Cloud, it ingests and stores real-time data streams at massive scale, and combines it with Salesforce data. This paves the way for highly personalized customer experiences Like1 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more Salesforce Government Cloud: Ensuring Compliance and Security Salesforce Government Cloud public sector solutions offer dedicated instances known as Government Cloud Plus and Government Cloud Plus – Defense. Read more

September 8, 2023in Salesforce Data Cloud, Salesforce Einstein

09Aug

Useful ChatGPT Techniques

Let’s embark on a journey through the intricate world of prompt engineering, guided by the tales of seasoned explorers who have braved the highs and lows of AI interactions. Picture these individuals as a daring voyager, charting unexplored territories to uncover the secrets of prompt mastery, all so that others may navigate these waters with ease. Useful ChatGPT Techniques. In this epic insight, our intrepid explorers shars a treasure trove of insights gleaned from their odyssey—a veritable “best of” plethora chronicling their conquests and not-so-conquests. From the peaks of success to the valleys of failure, every twist and turn in their adventure has led to the refinement of their craft. Prepare to be enthralled as they unravel the enigma of prompt design, revealing its pivotal role in shaping AI interactions. With each revelation, they unveil the power of perfect prompt design to elevate solutions, enchant customers, and conquer the myriad challenges that lie in wait. But this is no ordinary tale of technical prowess—no, dear reader, it is a grand odyssey teeming with intrigue and excitement. From the bustling streets of AI-powered applications to the untamed wilderness of off-topic queries, hallucinations, flat-out lies, and toxic language, our heroes navigate it all with cunning and finesse. Along the way, they impart their hard-earned wisdom, offering practical advice and cunning strategies to fellow travelers eager to tread the same path. With each chapter, they peel back the layers of mystery surrounding prompt engineering, illuminating the way forward for those brave enough to follow. So, dear reader, strap in and prepare for an adventure like no other. With our intrepid explorers as your guide, you’ll embark on a thrilling quest to unlock the secrets of prompt mastery and harness the full potential of AI-powered interactions. Why Prompt Design Matters Prompt design plays a crucial role in optimizing various aspects of your solution. A well-crafted prompt can: Let’s dive into the essential prompting approaches with the following table of contents: Prompts can be quite long and complex. Often, long, and carefully crafted prompts with the right ingredients can lead to a huge reduction in incorrectly processed user utterances. But always keep in mind that most prompt tokens have a price, i.e. the longer the prompt, the more expensive it is to call the API. Recently, however, there have been attempts to make prompt input tokens cheaper than output tokens. By mastering these prompting techniques, you can create prompts that not only enhance performance but also deliver exceptional customer experiences. Like Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

August 9, 2023in Salesforce Einstein

27Jul

How Data Cloud Vector Databases Work

How Data Cloud Vector Databases Work 1. Ingest Unstructured Data in Data Cloud With the help of a new, unstructured data pipeline, relevant unstructured data for case deflection, such as product manuals or upgrade eligibility knowledge articles, can be ingested in Data Cloud and stored as unstructured data model objects. 2. Chunk and Transform Data for Use in AI In Data Cloud, teams will then be able to select the data that they want to use in processes like search, chunking this data into small segments before converting it into embeddings – numeric representations of data optimized for use in AI algorithms. This is done through the Einstein Trust Layer, which securely calls a special type of LLM called an “embedding model” to create the embeddings. It is then indexed for use in search across the Einstein 1 platform alongside structured data. How Data Cloud Vector Databases Work. 3. Store Embeddings in Data Cloud Vector Database In addition to supporting chunking and indexing of data, Data Cloud now natively supports storage of embeddings – a concept called “vector storage”. This frees up time for teams to innovate with AI instead of managing and securing an integration to an external vector database. 4. Analyze and Act on Unstructured Data Use familiar platform tools like Flow, Apex, and Tableau to use unstructured data, such as clustering customer feedback by semantic similarity and creating automations that alert teams when sentiment changes significantly. 5. Deploy AI Search in Einstein Copilot to Deflect Cases With relevant data, such as knowledge articles, securely embedded and stored in Data Cloud’s vector database, this data can also be activated for use in Einstein AI Search within Einstein Copilot. When a customer visits a self-service portal and asks for details on how to return a product, for example, the Einstein Copilot performs semantic search by converting the user query into an embedding, after which it compares that query to the embedded data in Data Cloud, retrieving the most semantically relevant information for use in its answer while citing the sources it pulled from. The end result is AI-powered search capable of understanding the intent behind a question and retrieving not just article links but exact passages that best answer the question, all of which are summarized through a customer’s preferred LLM into a concise, actionable answer – boosting customer satisfaction while deflecting cases. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

July 27, 2023in Einstein 1 Platform, Salesforce Data Cloud

15May

Salesforce Data Cloud Explained

Salesforce Data Cloud, previously recognized as Salesforce CDP/Genie, made its debut at Dreamforce 2022, hailed by Salesforce as one of the most significant innovations in the company’s history. A hyperscale data platform built into Salesforce. Activate all your customer data across Salesforce applications with Data Cloud. Data Cloud facilitates the intake and storage of real-time data streams on a massive scale, empowering automated tasks that result in highly personalized experiences. Data can be sourced from diverse Salesforce data outlets, including Mulesoft, Marketing Cloud, and others, along with customers’ proprietary applications and data sources. Subsequently, it can dynamically respond to this real-time data by automating actions across Salesforce CRM, Marketing Cloud, Commerce, and more, inclusive of automating actions through Salesforce Flow. What is the Salesforce data cloud? Data Cloud is the fastest growing organically built product in Salesforce’s history (i.e. Salesforce built it themselves, not via acquisitions). Data Cloud could be described as the ‘Holy Grail of CRM’, meaning that the data problem that’s existed since the infancy of CRM is now finally solvable. Data Cloud is the foundation that speeds up the connectivity between different ‘clouds’ across the platform. However, Data Cloud is also a product that can be purchased. While not all Salesforce customers have licensed Data Cloud, being at the foundation means they are still taking advantage of Data Cloud to a degree – but this all becomes even stronger with Data Cloud as a personalization and data unification platform. What is the history of Data Cloud? Salesforce has gone through several iterations with naming its CDP product: Customer 360 Audiences → Salesforce CDP → Marketing Cloud Customer Data Platform → Salesforce Genie → Salesforce Data Cloud. In some instances, changes were made because the name just didn’t stick – but what’s more important to note, is that some of the name changes were to indicate the significant developments that happened to the product. Salesforce Data Cloud Differentiators Data Cloud, in itself, is impressive. While many organizations would consider it expensive, if you were to flip the argument on its head, by buying your own data warehouse, building the star schema, and paying for ongoing compute storage, you’d be looking to spend 5 to 10 times more than what Salesforce is charging for Data Cloud. Plus, data harmonization works best when your CRM data is front and center. There are other key differentiators that helps Data Cloud to stand out from the crowd: Is data cloud a data lakehouse? That means that Data Cloud is now not just a really good CDP, it’s now a data lake which will be used in sales and service use cases. But it also means that we can start to fundamentally move some of our higher-scale consumer products like Marketing and Commerce onto the platform. Is Snowflake a data Lakehouse? Snowflake offers customers the ability to ingest data to a managed repository, in what’s commonly referred to as a data warehouse architecture, but also gives customers the ability to read and write data in cloud object storage, functioning as a data lake query engine. What is the benefit of Salesforce data cloud? Data Cloud empowers Salesforce Sales Cloud with AI capabilities and automation that quickly closes deals and boosts productivity across every channel. It drives customer data from all the touchpoints and unifies it separately in individual customer profiles. Salesforce Data Cloud is a powerful data warehouse solution that allows companies to effectively manage and analyze their data. What is the difference between Salesforce CDP and data lake? Talking abut Salesforce CDP is a little bit like a history lesson. While a CDP provides a unified, structured view of customer data, a data lake, on the other hand, is more of a raw, unstructured storage repository that holds a vast amount of data (more than just customer data) in its native format until it’s needed. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

May 15, 2023in Mulesoft, Salesforce Data Cloud, Salesforce Einstein, Salesforce Implementation Services

Salesforce Spiff Announced

Generative AI Prompts with Retrieval Augmented Generation

LLM Knowledge Test

January ’24 Einstein Data Cloud Updates

Salesforce Enhances Einstein 1 Platform with New Vector Database and AI Capabilities

Retrieval Augmented Generation Techniques

Salesforce Einstein 1 Platform

Fine Tune Your Large Language Model

RAG – Retrieval Augmented Generation in Artificial Intelligence

Salesforce Data Cloud Evolution

Useful ChatGPT Techniques

How Data Cloud Vector Databases Work

Salesforce Data Cloud Explained

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