RAG evaluation and optimization
Know whether your RAG system works before users have to guess
Build a repeatable evaluation program that separates retrieval quality from answer quality and turns real user questions into release criteria.
Source-connected
Permission-aware
Measured before launch
01
Why RAG evaluation needs more than a thumbs-up
A plausible answer can be incomplete, unsupported, based on the wrong version, or correct for the wrong reason. Aggregate user feedback rarely identifies which component failed. A useful evaluation program measures the retrieval and generation stages separately, then examines the complete user task.
Evaluation begins with representative questions, authoritative sources, expected evidence, risk categories, and clear scoring guidance. Automated judges can accelerate testing, but expert review remains important for high-impact domains and ambiguous tasks.
02
What to measure
Retrieval recall and relevance at useful cutoffs
Ranking quality and presence of authoritative sources
Answer correctness and completeness
Faithfulness: whether claims are supported by retrieved context
Citation correctness and citation coverage
Refusal and uncertainty behavior when evidence is absent
Performance across user roles, topics, and document types
Latency, token use, model cost, and failure rates
03
An evaluation lifecycle
Create the test set
Sample real questions, expected sources, answer requirements, and important failure cases.
Establish a baseline
Record current search or workflow performance before tuning.
Experiment systematically
Change one retrieval, prompt, model, or indexing variable and compare.
Monitor production
Add reviewed failures and new question patterns back into regression tests.
04
Evaluation is the asset that reduces future lock-in
Models and retrieval technologies will change. A well-designed evaluation set lets your team compare replacements, upgrades, and configuration changes using business-relevant evidence instead of starting the decision over each time.
05
Build evaluation into release management
Store the test set, expected evidence, scoring guidance, system configuration, and results with version history. Run regression checks when documents, parsers, embeddings, indexes, retrieval logic, prompts, models, or permissions change. Production failures should become reviewed test cases.
Questions
Frequently asked questions
A practical next step
Bring us the workflow that is stuck—not a finished AI specification.
We will help clarify the opportunity, data, risks, and smallest useful way to prove value.