Agentic AI vs. Copilots: Defining the Future of Generative AI Artificial Intelligence has rapidly evolved, progressing from simple automation to generative models, to copilots. But now, a new player—Agentic AI—has emerged, promising to redefine the AI landscape. Is Agentic AI the next logical step, or will it coexist alongside copilots, each serving distinct roles? Copilots
AI Research Agents: Transforming Knowledge Discovery by 2025 (Plus the Top 3 Free Tools) The research world is on the verge of a groundbreaking shift, driven by the evolution of AI research agents. By 2025, these agents are expected to move beyond being mere tools to becoming transformative assets for knowledge discovery, revolutionizing industries such
A recent discovery by programmer and inventor Michael Calvin Wood is addressing a persistent challenge in AI: hallucinations. These false or misleading outputs, long considered an inherent flaw in large language models (LLMs), have posed a significant issue for developers. However, Wood’s breakthrough is challenging this assumption, offering a solution that could transform how AI-powered
10 AI Prompts to Accelerate Academic Reading with ChatGPT and Claude AI In the era of information overload, keeping pace with academic research can feel daunting. Tools like ChatGPT and Claude AI can streamline your reading and help you extract valuable insights from research papers quickly and efficiently. These AI assistants, when used ethically and
Salesforce Data Quality Challenges and AI Integration Salesforce is an incredibly powerful CRM tool, but like any system, it’s vulnerable to data quality issues if not properly managed. As organizations race to unlock the power of AI to improve sales and service experiences, they are finding that great AI requires great data. Let’s explore some
Artificial intelligence (AI) has moved from an emerging technology to a mainstream business imperative, making it essential for leaders across industries to understand and communicate its concepts. To help you unlock the full potential of AI in your organization, this 2024 AI Glossary outlines key terms and phrases that are critical for discussing and implementing
Harnessing AI for Enhanced Sales and Service: The Role of Data Organizations are racing to leverage AI to enhance their sales and service experiences. The Role of Data to Harness AI cannot be underestimated. However, great AI solutions rely on quality data. Traditionally, companies have managed structured data—neatly organized into rows and columns, such as
Significant Changes in Context Windows and Token Costs The first significant change in the AI world is the substantial increase in the context window size and the decrease in token costs. For example, the context window size of the largest model, Claude from Anthropic, exceeds 200,000 tokens. According to the latest news, Gemini’s context window
Andrew Best wrote in Artificial Intelligence in Plain English that LLMs are gullible. Article summarized below. LLMs are gullible, which is why various experiments are often conducted on them to observe their reactions to different types of prompts. Through extensive experimentation, more insight is gained into their actual functioning. Today, a humorous discovery was made
Enhancing Retrieval-Augmented Generation (RAG) Systems with Topic-Based Document Segmentation Dividing large documents into smaller, meaningful parts is crucial for the performance of Retrieval-Augmented Generation (RAG) systems. RAG Chunking Method. These systems benefit from frameworks that offer multiple document-splitting options. This Tectonic insight introduces an innovative approach that identifies topic changes using sentence embeddings, improving the
The applications of Retrieval-Augmented Generation (RAG) are diverse and expanding rapidly. Use Cases for Retrieval-Augmented Generation. Here are some key examples of how and where RAG is being utilized: Search Engines Search engines have implemented RAG to deliver more accurate and up-to-date featured snippets in their search results. RAG is particularly useful for applications of
As AI continues to advance, designers, builders, and creators are confronted with profound questions about the future of applications and how users will engage with digital experiences. AI Design Beyond the Chatbot. Generative AI has opened up vast possibilities, empowering people to utilize AI for tasks such as writing articles, generating marketing materials, building teaching
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
Retrieval-Augmented Generation (RAG) in Real-World Applications Retrieval-augmented generation (RAG) is at the core of many large language model (LLM) applications, from companies creating headlines to developers solving problems for small businesses. Evaluating RAG With Needle in Haystack Test. Evaluating RAG systems is critical for their development and deployment. Trust in AI cannot be achieved without
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,
