Start with a minimal stack: a small vector database for document embeddings, an LLM API or open model, and a web front-end.
Chunk your documents, generate embeddings, and index them. On every question, search similar chunks and feed them to the model as context.
Add guardrails: limit prompt length, filter unsafe content, and show sources. Log prompts and responses to refine prompts and retrieval.
Iterate with user feedback. Focus on helpfulness, accuracy, and latency—optimize the slowest steps first.