1
Brief safely
Start with sanitized context and a concrete reliability decision.
RAG reliability
Find retrieval regressions before they become answer-quality incidents.
RAG systems often fail at the boundary between retrieval, ranking, prompting, and answer policy. This service focuses on the evidence needed to tell whether retrieval changes improved the workflow or moved failure elsewhere.
Sanitized brief first
No private repo in form
Artifacts delivered
Ship / revise / stop memo
2
Reproduce failures
Turn traces, examples, or eval runs into repeatable evidence.
3
Define gates
Separate blockers, warnings, thresholds, and ownership.
4
Decide ship / revise / stop
Package the evidence into an engineering-readable memo.
Good fit
- Retrieval changes make answer quality hard to compare across releases.
- Offline scores do not match incidents reported by users, reviewers, or support teams.
- The team needs a replayable eval surface for candidate retrievers, prompts, or context policies.
Boundaries
- Does not require raw customer records in the public intake flow.
- Does not claim a universal RAG score; gates are tied to the workflow and risk.
- Does not replace product judgment where the right answer depends on policy or support ownership.
Deliverables
- Retrieval and answer-quality evaluation plan
- Replay set structure for representative tasks or traces
- Failure taxonomy for stale, missing, irrelevant, or unsafe context
- Release-gate criteria for retrieval or answer-policy changes
Inspect the artifact shape
The sample is representative structure, not client data. It shows how evaluation plans, taxonomies, gates, and decision memos can be packaged without inventing proof.
View artifact sampleProcess
How the work moves from brief to evidence.
01
Map the RAG workflow boundary and current decision point.
02
Select sanitized examples that represent the failure modes worth testing.
03
Separate retrieval failure, context assembly failure, answer failure, and policy failure.
04
Turn repeated failures into gates that can block or warn on release.
Next step
Have a workflow ready to evaluate?
Send sanitized context first, then use the call to confirm fit, access boundaries, and scope.
Related technical notes
Further reading for this service.
RAG Regression Testing: What to Track Before Retrieval Changes
A concise checklist for tracking RAG regressions across retrieval, context assembly, answer quality, and release gates.
Why Offline Scores Miss Production AI Failures
Why offline metrics can miss production AI failures, and how replay rows and failure taxonomies make evaluation more actionable.