A company is creating a chatbot to answer HR policy questions using a large language model and a large set of digital documents. Which technique will best improve the relevance of the generated answers?

Retrieval Augmented Generation (RAG) is the best technique because it allows the LLM to retrieve relevant information from a specific knowledge base (the company's HR documents) before generating an answer. This ensures the chatbot's responses are grounded in factual, up-to-date company policies, significantly improving relevance and reducing hallucinations. Few-shot prompting provides examples to guide the model's style and format, but doesn't introduce new factual knowledge. Setting the temperature to 1 makes the output more creative and less deterministic, which is undesirable for factual HR answers. Decreasing the token size limits the length of the response, but doesn't inherently improve its relevance.