Building Production RAG Systems

A useful retrieval-augmented generation system is not a chat UI with a vector database. It is a measured workflow for finding relevant context, grounding an answer, and detecting failure.

Production Checklist

• Define the questions the system must answer and the documents it can trust.
• Chunk documents by meaning, not arbitrary token counts alone.
• Store source metadata so every answer can cite where it came from.
• Evaluate retrieval quality before tuning prompts.
• Log unanswered questions and use them to improve the corpus.

Portfolio Deliverable

For a learner project, ship a small RAG app with ingestion notes, retrieval examples, evaluation cases, and a README explaining what fails and what you would improve next.