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