Retrieval-Augmented Generation Architecture Primer

Retrieval-Augmented Generation (RAG) is often pitched as a silver bullet, but the architecture choices you make early determine whether the system remains trustworthy at scale. This primer captures the design guidance we share with clients building internal copilots.

Split Retrieval And Generation Concerns

Treat retrieval as its own service with caching, semantic scoring, and search observability. This lets you iterate on embeddings, filters, and re-ranking without destabilizing the generation layer. When teams blend these concerns, performance tuning becomes guesswork.

Map Data Trust Levels

Not all data is created equal. Label corpora by sensitivity, provenance, and update cadence. We employ policy-aware routers that decide which data collections a given query can touch. That keeps audit trails clean and prevents hallucinations that cite outdated policies.

Measure Relevance And Response Debt

Alongside latency and token usage, track qualitative scores from subject matter experts. Response debt—questions the system cannot yet answer—guides backlog prioritization. With a shared scorecard, stakeholders see tangible progress while understanding current constraints.