RAG Integration Playbook
Retrieval-Augmented Generation works when you treat it like a system: data quality, retrieval design, evaluation, and observability. This is the checklist we use to ship RAG that stays trustworthy under real usage.
Get the RAG Integration Checklist (PDF)
The practical checklist we use when shipping RAG systems. Covers ingestion, retrieval, reranking, evals, security, and production deployment.
When RAG is the right tool
- • Answers must reflect internal docs or changing data.
- • You need citations/traceability for trust and debugging.
- • Users ask high-variance questions (support, internal enablement).
- • You want controllable cost/latency vs fine-tuning everything.
Architecture (production baseline)
A typical production RAG system has explicit stages with logging and testability. Avoid “single prompt + vector search” implementations that can’t be debugged.
- • Ingestion pipeline: parsing → chunking → metadata → embedding → indexing.
- • Retrieval: metadata filters → hybrid search → reranking → context assembly.
- • Generation: grounded answering with citations and refusal behavior.
- • System controls: caching, rate limits, fallbacks, and cost/latency routing.
- • Observability: traces, retrieval diagnostics, and eval-driven iteration.
Ingestion & indexing
- • Use document IDs + versioning so you can re-index safely.
- • Preserve hierarchy (doc title, heading path) in metadata for better retrieval.
- • Tune chunking for your content type (policies vs tickets vs code).
- • Store raw text and extracted fields separately for auditability.
Retrieval that works
- • Start with metadata filters (tenant, product area, recency).
- • Prefer hybrid search (BM25 + vectors) when text is precise.
- • Add reranking for relevance and to reduce “good embedding, wrong answer” errors.
- • Log retrieval diagnostics: query, top-k IDs, scores, rerank deltas.
Evals & regression testing
The fastest way to lose trust is “it was good last week.” A small eval harness keeps changes safe.
- • Build a golden set of queries with expected citations or allowed sources.
- • Track both retrieval quality (did we fetch the right chunk?) and answer quality.
- • Run evals on ingestion changes, chunking changes, and model/router changes.
- • Add online feedback signals: thumbs + “cite your source” failures.
Security & compliance patterns
- • Tenant isolation in retrieval (filters must be non-bypassable).
- • Audit logs: which docs were accessed, which tools were called.
- • PII-safe options: redaction, scoped retention, and access controls.
- • Tool calling: strict allowlists + parameter validation + safe fallbacks.