Kern AI’s Post

The Case for a 'Data-Centric RAG' Approach in Your AI Application Retrieval Augmented Generation (RAG) aims to equip Large Language Models (LLMs) like ChatGPT with more contextual knowledge on specialized topics, ensuring they have the necessary information to provide responses that are not only useful but accurate. Let's dive in. 🔍 Let's start with a straightforward example of RAG in action with ChatGPT - see the attached image. We asked ChatGPT the same question twice. Initially, without extra details, ChatGPT provided a general answer based on its training data. On the right, we posed the same question again but included additional context. This allowed ChatGPT to combine its pre-existing knowledge with the new information, yielding a more relevant and insightful response. ❌ Why Can RAG Sometimes Fail? The effectiveness of RAG heavily relies on the additional context from your input data. Given the complexity of language models, certain approaches are more successful than others. Insufficient data preparation can lead to inaccurate AI-generated content and hallucinations. 🏆 The Importance of a 'Data-Centric RAG' Approach to Prevent Hallucinations Adopting a data-centric approach to Retrieval-Augmented Generation (RAG) highlights the importance of the underlying data that fuels Large Language Models (LLMs). This strategy focuses on the quality, relevance, and diversity of the data used to inform these models, significantly reducing inaccurate responses and hallucinations. 📕 We have developed a detailed 60-page guide on strategies to minimize and manage the risk of hallucinations in Generative AI. The guide underscores the necessity of modeling and structuring your data with a "Data-Centric RAG Approach." If you're keen on exploring practical strategies and enhancing your understanding, you can download the guide here: https://lnkd.in/ewqXXSMp

I've been blown away by Anthropic's new Claude 3.5 Sonnet model and the newly released Artifacts feature. The speed and ease of use, even for complex tasks, is unlike anything I've seen from an LLM since I was able to subscribe to ChatGPT Plus. While the rapid progress in AI and LLMs is mind-boggling, we can't fall into the trap of only thinking about chatbots. These models are insanely versatile - going toe-to-toe with GPT-4o on all kinds of challenges. Chatbots are cool, but they're just scratching the surface. I think Artifacts is pointing to where this is all going, but the real game-changer will be when these models get integrated into the very heart of how we collaborate and create value. It won't just be about fancier chatbots - it'll be an entirely new paradigm of AI-native knowledge work. And from what I've seen, Anthropic is charging hard in that direction. Buckle up, it's going to be a wild ride! https://lnkd.in/eaufXMfq

Intrigued by the constant evolution in the AI space? Dive into the latest advancements that are re-shaping our digital interactions: 1. Small language models are emerging as the next frontier in AI. These scaled-down versions retain the power of their larger counterparts without the heavy resource demands. As multi-choice question and reasoning tasks continue to excel, the nimbleness of small models lends to broader applications. [Read more at VentureBeat](https://lnkd.in/d2dFa8uC) 2. ChatGPT receives a boost in human-like interactions with a fresh update strategy, including notable enhancements in writing, mathematical capabilities, and logical reasoning. Experience the future of conversational AI as it becomes smoother and more intuitive. [Discover the details at Financial Express](https://lnkd.in/ddvgReXC) 3. GPT-4 is morphing into a more concise communicator with the arrival of GPT-4 Turbo. Promising less verbosity and more conversational exchanges, this update is gearing to revolutionize the efficiency and clarity of AI-driven dialogue. [Uncover the insights at Business World](https://lnkd.in/dTjDkeXM) As these advances paint a dynamic landscape for AI's future, we can expect to encounter AI personalities that are not just smart, but also more adaptive and responsive to human needs, fostering a synergy that could dramatically redefine our interaction with technology. The horizon looks promising as we venture further into this AI-enhanced epoch!

Good article from VentureBeat that explains "Why multi-agent AI tackles complexities LLMs can’t". The article highlights how multi-agent AI systems overcome LLMs' constraints like outdated knowledge, limited reasoning, and static nature. These systems utilize agents that specialize in tasks, employing tools, memory, reasoning, and action to tackle complex workflows. By enabling collaborative and iterative problem-solving, multi-agent frameworks offer scalable, adaptive solutions for real-world challenges, making them a pivotal advancement in AI technology #multiagentai #largelanguagemodel #generativeai #artificialintelligence https://lnkd.in/g5JYu6kC

OpenAI unveils GPT-4o, an #AI integrating text, speech and vision. The #ChatGPT-powering model enables real-time #multimodal interactions. 👩💻🤖 Read: https://lnkd.in/dXb-JxXa

Here's something AI can't do: use your product as an expert, and tell you what works & what doesn't work about it. Even when I use AI tools these days I do so as an artist, and constantly find the edge of the capability of the technology. That's because as a human artist I have a whole host of data (if you need to think of it clinically like that) as well as a personality which I express intentionally as well as unintentionally. AI (as we have it now) doesn't have that capacity. It will even tell you that if you get into a philosophic conversation with one of these LLM's. Plus, no matter how intelligent the machine is, it always performs better when given good linguistic prompts. That's something I'm expert at. TEST ME ;)

The study of AI is the study of our own reflections in the mirror. As we dig deeper into higher AI capabilities, we will learn to stop defining the value of humanity with its assumed rarity, but rather with its position in the universe.

Discover the latest published post by Pankaj, our Chief Innovation Leader! Learn more on why this will be a powerful blend that shapes the future of solid applications of AI, providing the foundational building blocks for companies to utilize AI to develop innovative new products and services. Don't hesitate to reach out to find out more about how 3Pillar can help you navigate this shift. https://lnkd.in/ecGxuWKu #AI #artificialintelligence

AI doesn’t ‘lie’; it mirrors the clarity—or lack thereof—in how we communicate with it. Just like getting to know a new person, understanding how to guide AI requires precision in how we frame our questions and structure our interactions. The emerging discipline of prompt engineering isn’t just a technical skill—it’s a way of building trust and unlocking AI's potential as a reliable partner. This isn’t just about avoiding mistakes; it’s about setting the foundation for AI systems that can support critical decisions. The way we approach this now will shape the future of AI as a dependable tool in both the public and private sectors. Read more below: https://lnkd.in/dnvyK9Ad

Fascinating article about using multi agent AI in unique process steps to improve efficiency. What could you use this approach for?