Retrieval-augmented generation (RAG) is the most reliable way to put an LLM to work on your proprietary knowledge. This guide covers the production architecture that keeps answers grounded, evaluated, and monitored.
RAG architecture at a glance
- Ingest and chunk your documents thoughtfully (not arbitrary splits)
- Embed and store in a vector database — pgvector, Pinecone, or Weaviate
- Retrieve relevant context and construct a grounded prompt
- Generate with citations, confidence thresholds, and guardrails
- Serve via FastAPI with logging and monitoring
Where RAG apps go wrong
Poor chunking and retrieval
Bad chunking is the #1 cause of wrong answers. Chunk by semantic boundaries, test retrieval quality, and consider re-ranking for precision.
No evaluation
Build an evaluation set of real questions with expected answers. Measure retrieval and answer quality before and after every change — otherwise you're tuning blind.
Missing guardrails
Confidence thresholds, PII handling, and human fallback for sensitive intents keep the system trustworthy. An ungrounded LLM will answer confidently even when it shouldn't.
Hosted vs custom
For most applications, a hosted LLM (OpenAI/Anthropic) plus good retrieval outperforms fine-tuning or training from scratch — at a fraction of the cost. Reserve custom models for clear data, cost, or privacy reasons.
Ship it right
Our Python AI/ML team builds RAG apps with evaluation and monitoring — start with a PoC to validate feasibility on your data.