RAG fit check
Retrieval-augmented generation solves a specific problem. Three checks that tell you whether you have that problem — before you build the pipeline.
RAG earns its complexity when answers must be grounded in knowledge that keeps changing. It fails quietly when the corpus is weak, permissions are unclear, or nobody measures whether retrieval finds the right passages at all.
The corpus decides more than the model
Stale documents, contradicting versions, unclear ownership and permission boundaries will sink a RAG system before the embedding model gets a chance to matter. Audit the knowledge first; pick the model later.
Measure retrieval before generation
If the right passages aren't found reliably, better generation only makes wrong answers sound more confident. Retrieval quality is measurable — build that evaluation before tuning anything downstream.
Design what happens when the system doesn't know
Missing evidence, stale content, a question outside the corpus — each needs a designed behavior: decline, cite with a warning, or route to a human. The unplanned alternative is improvisation delivered in a confident tone.
Case studies behind this
- Round-trip translation of structured documents without breaking them
Translated files open like human-edited originals: same structure, same formatting, and no silent reflow or document corruption.
- A multi-provider AI gateway with built-in cost accounting
Model choice became configuration, new providers inherited common controls, and spend became visible while requests were still running.
Related services
RAG systems and knowledge workflows
Start here when knowledge lives in documents, tickets or transcripts, and answers need grounding, citations and boundaries.
Open serviceLLM productionization
Start here when the prototype is convincing but nobody can say what it costs at scale, why it fails, or whether yesterday's prompt change made it worse.
Open service