In this episode we discuss the hype around AI and the challenges in achieving its full potential in 2024. The last 10% of solving problems with AI has proven to be difficult due to LLM hallucinations and reliability challenges. We discuss how this problem can be addressed by grounding LLMs with a knowledge base via the paradigm of Retrieval Augmented Generation (RAG). We discuss the different approaches to working with language models, including training from scratch, fine-tuning, and using RAG, and the opportunities for entrepreneurs in the AI space. Takeaways Generative AI may be the next major platform since the internet and mobile, but we are coming down from the peak of inflated expectations of the Gen AI hype cycle LLMs are general purpose models, and when asked domain-specific questions, LLMs tend to “hallucinate” (i.e. generate plausible-sounding answers) rather than admit ignorance Grounding in facts and providing relevant context can help mitigate the hallucination problem. Retrieval Augmented Generation (RAG) is a common paradigm for grounding LLMs in facts. As AI models and agents become commoditized and democratized, competitive moats will be built around proprietary data and tailored user experiences

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