W2 5 Retrieval Augmented Generation RAG

This episode explores Retrieval Augmented Generation (RAG), a technique enhancing Large Language Model (LLM) capabilities. Against the backdrop of LLMs' limitations in accessing specific information, RAG offers a solution by incorporating relevant documents into the prompt, effectively augmenting the LLM's knowledge base. The process involves three steps: retrieving relevant documents, incorporating the retrieved text into an updated prompt, and finally, prompting the LLM with this enriched prompt. For instance, answering a question about employee parking requires selecting the relevant company policy document and including it in the prompt. More significantly, this approach reframes LLMs not as knowledge stores but as reasoning engines, processing provided information to generate answers rather than relying solely on pre-existing knowledge. This perspective opens up new applications and transforms web search, as seen in examples like Microsoft Bing and Google's generative AI features. The episode concludes by highlighting the practical applications of RAG in various software and web interfaces, emphasizing its potential for future development and its current use in transforming web search.