In this episode, the hosts discuss RAG (Retrieval Augmented Generation) and its importance for new generative AI applications. They explain that RAG is a technique that enhances language models by adding context and relevant information from external sources. RAG helps combat the problem of hallucinations, where language models generate incorrect or made-up information.

The hosts also highlight the importance of reducing hallucinations within a reasonable limit and setting clear expectations with clients. They discuss the use cases of RAG, such as adding context to LLMs, resurrecting old documentation, and improving search and product discovery in e-commerce. The conversation focused on the implementation and use cases of Retrieval-Augmented Generation (RAG).

The main themes discussed were the process of embedding documents, handling longer data sources, chunking information, and the generation of responses. The conversation also touched on the customization of RAG, the three levers of customization (chunking, vector similarity search, and prompting), and the potential of RAG as a product or feature. Use cases for RAG in revenue generation were explored, including data extraction and AI dev tools. The conversation concluded with a call to explore RAG further and join the DIY AI movement.

• RAG enhances language models by adding context and relevant information from external sources.
• RAG helps combat the problem of hallucinations in language models.
• Reducing hallucinations within a reasonable limit is important, and clear expectations should be set with clients.
• RAG has various use cases, including adding context to LLMs, resurrecting old documentation, and improving search and product discovery in e-commerce. RAG involves the process of embedding documents and using vector similarity search to retrieve relevant information.
• Chunking is necessary for handling longer data sources, such as books or large documents, and allows for efficient retrieval.
• RAG can be customized through the levers of chunking, vector similarity search, and prompting.
• RAG has various use cases for revenue generation, including data extraction and AI dev tools.
• RAG is an emerging field with opportunities for DIY exploration and experimentation.