How RAG in PHP Stops LLMs From Hallucinating Internal Policy Answers

Retrieval-Augmented Generation (RAG) addresses a key failure of large language models: confidently generating plausible but incorrect answers to internal policy questions. The approach works by first retrieving relevant documents from a knowledge base, then instructing the model to answer strictly from those results. A PHP implementation called NanoAgent demonstrates this using a simple keyword-search tool against an in-memory array, requiring no vector database to get started. Three system prompt rules enforce safety: answer only from retrieved documents, decline if information is absent, and always cite the source document. The architecture is designed to scale — the same agent and tool interface can later back onto SQL tables or vector stores like Qdrant without changing the agent code.