AI systems · 7 min read
RAG is easy to demo. Reliability is the actual product.
A retrieval demo can look convincing with ten clean documents and one happy path. Production introduces changing sources, permissions, conflicting facts, latency budgets, and questions no one predicted.
01
Retrieval quality starts upstream
Chunking and embeddings matter, but source quality, document structure, and metadata usually matter more. A system cannot retrieve a distinction the ingestion pipeline erased.
Build ingestion as an observable data product: version sources, record failures, preserve access rules, and make reprocessing safe.
- Source and chunk lineage
- Permission-aware retrieval
- Freshness and deletion guarantees
02
Measure the stages separately
A wrong answer can come from retrieval, ranking, context assembly, generation, or the underlying source. One blended score makes diagnosis slow.
Track retrieval recall, citation quality, groundedness, task completion, latency, and cost independently. Then teams can improve the right layer.
03
Design for uncertainty
The interface should not force confidence the system does not have. Useful fallbacks include asking a narrower question, presenting source passages, routing to a person, or explicitly declining.
Trust grows when the product is predictably honest—not when it answers every prompt.
Keep building.
Bring the constraint. We will tell you if we are the right studio.
