Brief safely
Sanitized context and one concrete reliability decision.
01RAG 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.
Reproduce
Turn traces, examples, or eval runs into replayable evidence.
Define gates
Separate blockers, warnings, thresholds, and ownership.
Leave a record
Connect the evidence to a ship, revise, or stop call.
02Scope ledger
What enters the engagement—and what leaves it.
A strong fit
01Retrieval changes make answer quality hard to compare across releases.
02Offline scores do not match incidents reported by users, reviewers, or support teams.
03The team needs a replayable eval surface for candidate retrievers, prompts, or context policies.
What your team keeps
01Retrieval and answer-quality evaluation planReusable within the agreed project scope.
02Replay set structure for representative tasks or tracesReusable within the agreed project scope.
03Failure taxonomy for stale, missing, irrelevant, or unsafe contextReusable within the agreed project scope.
04Release-gate criteria for retrieval or answer-policy changesReusable within the agreed project scope.
03Working sequence
From uncertain behavior to an inspectable decision.
The sequence is deliberately legible: agree the boundary, reproduce what matters, then make the release rule explicit.
Map the RAG workflow boundary and current decision point.
Select sanitized examples that represent the failure modes worth testing.
Separate retrieval failure, context assembly failure, answer failure, and policy failure.
Turn repeated failures into gates that can block or warn on release.
04Open method
Inspect the reasoning before you engage.
These notes expose the decision framework behind the work. They are methodology, not disguised client proof.
01Technical noteRAG Regression Testing: What to Track Before Retrieval ChangesA concise checklist for tracking RAG regressions across retrieval, context assembly, answer quality, and release gates.02Technical noteWhy Offline Scores Miss Production AI FailuresWhy offline metrics can miss production AI failures, and how replay rows and failure taxonomies make evaluation more actionable.
09Safe first step
Bring the decision your team needs to defend.
Describe the rag evaluation and reliability context in sanitized terms. We will use the first exchange to confirm fit, evidence available, and the safest next step.
No credentials, production data, customer records, or private repository access in the first brief.
Prefer to talk it through? Request a 30-minute call