AI evaluation
RAG evaluation
Retrieval and generation tested separately, then together. Enterprise knowledge bases change daily; the harness re-runs on every index update, so answer quality is a monitored property rather than a launch-day snapshot.
Discuss your AI initiativeScope of work
Process
How an engagement runs
01
Scope
We agree what to test, against which benchmarks and thresholds.
02
Harness
A reproducible test or evaluation harness is built in your repositories.
03
Execute
Suites run on every release; failures are triaged with your team.
04
Report
Findings, evidence and a remediation backlog, written for review.
Questions
Common questions
What is RAG evaluation?
Separate measurement of retrieval quality and answer faithfulness, then end-to-end gates on the combined system.
Why test retrieval separately from generation?
A wrong answer can come from a bad chunk or a bad generation. Separating the two halves makes the failure mode diagnosable.
Do you tune chunking and indexing?
Yes. Chunking and index experiments run against the evaluation harness, so changes are proven rather than assumed.