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Salesforce Enhances Einstein 1 Platform with New Vector Database and AI Capabilities
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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 14, 2023in Einstein 1 Platform, Salesforce
Retrieval Augmented Generation in Artificial Intelligence
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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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October 26, 2023in Einstein 1 Platform, Generative AI, Salesforce, Salesforce Data Cloud
Communicating With Machines
20Sep

Communicating With Machines

For as long as machines have existed, humans have struggled to communicate effectively with them. The rise of large language models (LLMs) has transformed this dynamic, making “prompting” the bridge between our intentions and AI’s actions. By providing pre-trained models with clear instructions and context, we can ensure they understand and respond correctly. As UX practitioners, we now play a key role in facilitating this interaction, helping humans and machines truly connect. The UX discipline was born alongside graphical user interfaces (GUIs), offering a way for the average person to interact with computers without needing to write code. We introduced familiar concepts like desktops, trash cans, and save icons to align with users’ mental models, while complex code ran behind the scenes. Now, with the power of AI and the transformer architecture, a new form of interaction has emerged—natural language communication. This shift has changed the design landscape, moving us from pure graphical interfaces to an era where text-based interactions dominate. As designers, we must reconsider where our focus should lie in this evolving environment. A Mental Shift In the era of command-based design, we focused on breaking down complex user problems, mapping out customer journeys, and creating deterministic flows. Now, with AI at the forefront, our challenge is to provide models with the right context for optimal output and refine the responses through iteration. Shifting Complexity to the Edges Successful communication, whether with a person or a machine, hinges on context. Just as you would clearly explain your needs to a salesperson to get the right product, AI models also need clear instructions. Expecting users to input all the necessary information in their prompts won’t lead to widespread adoption of these models. Here, UX practitioners play a critical role. We can design user experiences that integrate context—some visible to users, others hidden—shaping how AI interacts with them. This ensures that users can seamlessly communicate with machines without the burden of detailed, manual prompts. The Craft of Prompting As designers, our role in crafting prompts falls into three main areas: Even if your team isn’t building custom models, there’s still plenty of work to be done. You can help select pre-trained models that align with user goals and design a seamless experience around them. Understanding the Context Window A key concept for UX designers to understand is the “context window“—the information a model can process to generate an output. Think of it as the amount of memory the model retains during a conversation. Companies can use this to include hidden prompts, helping guide AI responses to align with brand values and user intent. Context windows are measured in tokens, not time, so even if you return to a conversation weeks later, the model remembers previous interactions, provided they fit within the token limit. With innovations like Gemini’s 2-million-token context window, AI models are moving toward infinite memory, which will bring new design challenges for UX practitioners. How to Approach Prompting Prompting is an iterative process where you craft an instruction, test it with the model, and refine it based on the results. Some effective techniques include: Depending on the scenario, you’ll either use direct, simple prompts (for user-facing interactions) or broader, more structured system prompts (for behind-the-scenes guidance). Get Organized As prompting becomes more common, teams need a unified approach to avoid conflicting instructions. Proper documentation on system prompting is crucial, especially in larger teams. This helps prevent errors and hallucinations in model responses. Prompt experimentation may reveal limitations in AI models, and there are several ways to address these: Looking Ahead The UX landscape is evolving rapidly. Many organizations, particularly smaller ones, have yet to realize the importance of UX in AI prompting. Others may not allocate enough resources, underestimating the complexity and importance of UX in shaping AI interactions. As John Culkin said, “We shape our tools, and thereafter, our tools shape us.” The responsibility of integrating UX into AI development goes beyond just individual organizations—it’s shaping the future of human-computer interaction. This is a pivotal moment for UX, and how we adapt will define the next generation of design. Content updated October 2024. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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

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September 20, 2023in Data
Salesforce Einstein and Einstein Automate
09Aug

Einstein Trust

Generative AI, Salesforce, and the Commitment to Trust The excitement surrounding generative AI is palpable as it unlocks new dimensions of creativity for individuals and promises significant productivity gains for businesses. Engaging with generative AI can be a great experience, whether creating superhero versions of your pets with Midjourney or crafting pirate-themed poems using ChatGPT. According to Salesforce research, employees anticipate saving an average of 5 hours per week through the adoption of generative AI, translating to a substantial monthly time gain for full-time workers. Whether designing content for sales and marketing or creating a cute version of a beloved story, generative AI is a tool that helps users create content faster. However, amidst the enthusiasm, questions arise, including concerns about the security and privacy of data. Users ponder how to leverage generative AI tools while safeguarding their own and their customers’ data. Questions also revolve around the transparency of data collection practices by different generative AI providers and ensuring that personal or company data is not inadvertently used to train AI models. Additionally, there’s a need for assurance regarding the accuracy, impartiality, and reliability of AI-generated responses. Salesforce has been at the forefront of addressing these concerns, having embraced artificial intelligence for nearly a decade. The Einstein platform, introduced in 2016, marked Salesforce’s foray into predictive AI, followed by investments in large language models (LLMs) in 2018. The company has diligently worked on generative AI solutions to enhance data utilization and productivity for their customers. The Einstein Trust Layer is designed with private, zero-retention architecture. Emphasizing the value of Trust, Salesforce aims to deliver not just technological capabilities but also a responsible, accountable, transparent, empowering, and inclusive approach. The Einstein Trust Layer represents a pivotal development in ensuring the security of generative AI within Salesforce’s offerings. The Einstein Trust Layer is designed to enhance the security of generative AI by seamlessly integrating data and privacy controls into the end-user experience. These controls, forming gateways and retrieval mechanisms, enable the delivery of AI securely grounded in customer and company data, mitigating potential security risks. The Trust Layer incorporates features such as secure data retrieval, dynamic grounding, data masking, zero data retention, toxic language detection, and an audit trail, all aimed at protecting data and ensuring the appropriateness and accuracy of AI-generated content. Salesforce proactively provided the ability for any admin to control how prompt inputs and outputs are generated, including reassurance over data privacy and reducing toxicity. This innovative approach allows customers to leverage the benefits of generative AI without compromising data security and privacy controls. The Trust Layer acts as a safeguard, facilitating secure access to various LLMs, both within and outside Salesforce, for diverse business use cases, including sales emails, work summaries, and service replies in contact centers. Through these measures, Salesforce underscores its commitment to building the most secure generative AI in the industry. Generating content within Salesforce can be achieved through three methods: CRM Solutions: Einstein Copilot Studio: Einstein LLM Generations API: An overarching feature of these AI capabilities is that every Language Model (LLM) generation is meticulously crafted through the Trust Layer, ensuring reliability and security. At Tectonic, we look forward to helping you embrace and utilize generative AI with Einstein save time. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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August 9, 2023in Artificial Intelligence, Data, Generative AI, Salesforce, Salesforce Einstein
Salesforce Document Generation
14Mar

Generative AI

Artificial Intelligence in Focus Generative Artificial Intelligence is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. What is the difference between generative AI and general AI? Traditional AI focuses on analyzing historical data and making future numeric predictions, while generative AI allows computers to produce brand-new outputs that are often indistinguishable from human-generated content. Recently, there has been a surge in discussions about artificial intelligence (AI), and the spotlight on this technology seems more intense than ever. Despite AI not being a novel concept, as many businesses and institutions have incorporated it in various capacities over the years, the heightened interest can be attributed to a specific AI-powered chatbot called ChatGPT. ChatGPT stands out by being able to respond to plain-language questions or requests in a manner that closely resembles human-written responses. Its public release allowed people to engage in conversations with a computer, creating a surprising, eerie, and evocative experience that captured widespread attention. This ability of an AI to engage in natural, human-like conversations represents a notable departure from previous AI capabilities. The Artificial Intelligence Fundamentals badge on the Salesforce Trailhead delves into the various specific tasks that AI models are trained to execute, highlighting the remarkable potential of generative AI, particularly in its ability to create diverse forms of text, images, and sounds, leading to transformative impacts both in and outside the workplace. Let’s explore the tasks that generative AI models are trained to perform, the underlying technology, and how businesses are specializing within the generative AI ecosystem. It also delves into concerns that businesses may harbor regarding generative Artificial Intelligence. Exploring the Capabilities of Language Models While generative AI may appear as a recent phenomenon, researchers have been developing and training generative AI models for decades. Some notable instances made headlines, such as Nvidia unveiling an AI model in 2018 capable of generating photorealistic images of human faces. These instances marked the gradual entry of generative AI into public awareness. While some researchers focused on AI’s capabilities generating specific types of images, others concentrated on language-related AI. This involved training AI models to perform various tasks related to interpreting text, a field known as natural language processing (NLP). Large language models (LLMs), trained on extensive datasets of real-world text, emerged as a key component of NLP, capturing intricate language rules that humans take years to learn. Summarization, translation, error correction, question answering, guided image generation, and text-to-speech are among the impressive tasks accomplished by LLMs. They provide a tool that significantly enhances language-related tasks in real-world scenarios. Predictive Nature of Generative AI Despite the remarkable predictions generated by generative AI in the form of text, images, and sounds, it’s crucial to clarify that these outputs represent a form of prediction rather than a manifestation of “thinking” by the computer. Generative Artificial Intelligence doesn’t possess opinions, intentions, or desires; it excels at predicting sequences of words based on patterns learned during training. Understanding this predictive nature is key. The AI’s ability to predict responses aligns with expectations rather than reflecting any inherent understanding or preference. Recognizing the predictive character of generative AI underscores its role as a powerful tool, bridging gaps in language-related tasks for both professional and recreational purposes. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 14, 2023in Salesforce Einstein
tectonic logo
14Nov

AI Large Language Models

What Exactly Constitutes a Large Language Model? Picture having an exceptionally intelligent digital assistant that extensively combs through text, encompassing books, articles, websites, and various written content up to the year 2021. Yet, unlike a library that houses entire books, this digital assistant processes patterns from the textual data it undergoes. This digital assistant, akin to a large language model (LLM), represents an advanced computer model tailored to comprehend and generate text with humanlike qualities. Its training involves exposure to vast amounts of text data, allowing it to discern patterns, language structures, and relationships between words and sentences. How Do These Large Language Models Operate? Fundamentally, large language models, exemplified by GPT-3, undertake predictions on a token-by-token basis, sequentially building a coherent sequence. Given a request, they strive to predict the subsequent token, utilizing their acquired knowledge of patterns during training. These models showcase remarkable pattern recognition, generating contextually relevant content across diverse topics. The “large” aspect of these models refers to their extensive size and complexity, necessitating substantial computational resources like powerful servers equipped with multiple processors and ample memory. This capability enables the model to manage and process vast datasets, enhancing its proficiency in comprehending and generating high-quality text. While the sizes of LLMs may vary, they typically house billions of parameters—variables learned during the training process, embodying the knowledge extracted from the data. The greater the number of parameters, the more adept the model becomes at capturing intricate patterns. For instance, GPT-3 boasts around 175 billion parameters, marking a significant advancement in language processing capabilities, while GPT-4 is purported to exceed 1 trillion parameters. While these numerical feats are impressive, the challenges associated with these mammoth models include resource-intensive training, environmental implications, potential biases, and more. Large language models serve as virtual assistants with profound knowledge, aiding in a spectrum of language-related tasks. They contribute to writing, offer information, provide creative suggestions, and engage in conversations, aiming to make human-technology interactions more natural. However, users should be cognizant of their limitations and regard them as tools rather than infallible sources of truth. What Constitutes the Training of Large Language Models? Training a large language model is analogous to instructing a robot in comprehending and utilizing human language. The process involves: Fine-Tuning: A Closer Look Fine-tuning involves further training a pre-trained model on a more specific and compact dataset than the original. It is akin to training a robot proficient in various cuisines to specialize in Italian dishes using a dedicated cookbook. The significance of fine-tuning lies in: Versioning and Progression Large language models evolve through versions, with changes in size, training data, or parameters. Each iteration aims to address weaknesses, handle a broader task spectrum, or minimize biases and errors. The progression is simplified as follows: In essence, large language model versions emulate successive editions of a book series, each release striving for refinement, expansiveness, and captivating 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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November 14, 2022in Data, Salesforce, Salesforce Einstein, Technology
Salesforce CRM for AI driven transformation
22Mar

Salesforce Artificial Intelligence

Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust AI technologies directly into users’ workspaces. The Einstein Platform offers administrators and developers a comprehensive suite of platform services, empowering them to create smarter applications and tailor AI solutions for their enterprises. What is the designated name for Salesforce’s AI? Salesforce Einstein represents an integrated array of CRM AI technologies designed to facilitate personalized and predictive experiences, enhancing the professionalism and attractiveness of businesses. Since its introduction in 2016, it has consistently been a leading force in AI technology within the CRM realm. Is Salesforce Einstein a current feature? “Einstein is now every customer’s data scientist, simplifying the utilization of best-in-class AI capabilities within the context of their business.” Is Salesforce Einstein genuinely AI? Salesforce Einstein for Service functions as a generative AI tool, contributing to the enhancement of customer service and field service operations. Its capabilities extend to improving customer satisfaction, cost reduction, increased productivity, and informed decision-making. Salesforce Artificial Intelligence AI is just the starting point; real-time access to customer data, robust analytics, and business-wide automation are essential for AI effectiveness. Einstein serves as a comprehensive solution for businesses to initiate AI implementation with a trusted architecture that prioritizes data security. Einstein is constructed on an open platform, allowing the safe utilization of any large language model (LLM), whether developed by Salesforce Research or external sources. It offers flexibility in working with various models within a leading ecosystem of LLM platforms. Salesforce’s commitment to AI is evident through substantial investments in researching diverse AI areas, including Conversational AI, Natural Language Processing (NLP), Multimodal Data Intelligence and Generation, Time Series Intelligence, Software Intelligence, Fundamentals of Machine Learning, Science, Economics, and Environment. These endeavors aim to advance technology, improve productivity, and contribute to fields such as science, economics, and environmental sustainability. Content updated April 2023. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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March 22, 2017in Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein, Technology

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