When an LLM chatbot answers policy questions, it must find specific information matching the user's intent across thousands of documents. This retrieval problem requires the model to understand meaning beyond keyword matching, handling policy variations and outdated rules. The solution involves retrieval-augmented generation (RAG), where an embedding model converts questions into numerical representations.

A search system then finds promising passages from a vector database, supplying original text to the LLM for contextually accurate answers. Crucially, documents must be divided into smaller units called chunks to balance precision and context. A travel handbook covering flights, accommodation, and insurance, when treated as a single item, produces broad representations that obscure specific rules.

Smaller chunks focusing on particular topics—like hotel reimbursement versus approval requirements—allow the system to identify exact passages rather than entire documents. This chunking strategy creates a balance between retrieving sufficient context and maintaining specificity. The article explores how LLMs can effectively locate critical information within large document collections, ensuring answers are both accurate and cite valid sources.

It covers embedding models, search systems, and the practical implementation of RAG patterns for reliable LLM applications across diverse knowledge bases.