The latest episode of our podcast is out! We had Nathan Lambert from HuggingFace on to discuss RLHF, LLM evaluations, and how to improve discussion around AI research. Check it out! Retrieval-augmented generation (RAG) is the most popular technique for infusing LLMs with private or proprietary data. RAG applications allow you to use a private set of documents to contextualize your prompts — for example, you might build a chatbot that answers questions about your company’s internal wiki. The phrase “retrieval augmentation” is derived from searching for the most relevant documents to the user’s question. Those documents are then combined into a prompt and passed into an LLM to answer the query based on the retrieved documents.