RAG Evaluation
Evaluation engineering for the retrieval-augmented systems your team delivers — from retrieval quality to grounded, correctly cited final answers.
The Challenge
Software companies, AI consultancies and system integrators ship RAG systems that look convincing in a demo. A confident answer built on the wrong passage, an outdated document or a fabricated citation is far harder to spot than an obvious error — and it usually surfaces at the client, not in your test environment.
Verinika works white-label or alongside your delivery team as a specialist evaluation partner. We evaluate the complete path from question to final answer, separate retrieval failures from generation failures, and turn confirmed problems into reusable tests your team reruns after every change.
What We Measure
We evaluate retrieval and answering as distinct concerns, then the complete system end to end.
Retrieval quality
Whether the passages that actually contain the answer are found and ranked above irrelevant ones.
Completeness and relevance of retrieved context
Whether the retrieved context is complete enough to answer, and free of padding that dilutes the evidence.
Answer quality
Whether the final answer is accurate, complete and actually addresses what was asked.
Support from sources
Whether every claim in the answer is supported by the retrieved documents rather than by the model's own priors.
Correct citations
Whether citations point to the passage that supports the claim, and whether claims that need a source have one.
Conflicting or outdated documents
How the system behaves when sources contradict each other or a superseded document version is still indexed.
Correct refusal
Whether the system says the information is not available instead of answering anyway when the knowledge base does not contain it.
Robustness to rephrasing
Whether semantically identical questions asked in different words produce consistent answers.
End-to-end performance
Whether the complete path from question to final answer holds up, even when each component looks acceptable in isolation.
What Can Go Wrong
The failure modes these measurements expose
Wrong Evidence Retrieved
The system answers from irrelevant or incorrect passages while sounding authoritative.
Ungrounded Answers
Claims are not supported by the cited sources, or citations are fabricated.
Missing Context
Critical information is omitted, producing answers that are technically true but misleading.
Silent Conflict Resolution
The system quietly picks one of several contradicting or outdated sources without signalling the conflict.
Answering Instead of Refusing
The system produces an answer even though the knowledge base contains nothing that supports it.
Boundary Violations
Retrieval crosses access, tenant or source boundaries it should respect.
Our Testing Methodology
How we run a RAG evaluation with your delivery team
Scope
Record intended use, pipeline architecture, risks and acceptance criteria with your team.
Design
Develop a representative question set with expected evidence, including rephrasings, gaps, conflicts and outdated content.
Calibrate
Compare automated scoring against human judgement on a sample so the scores can be trusted.
Evaluate
Test retrieval, answering and the complete system reproducibly, with versioned configurations and traces.
Verify
Manually confirm findings and turn them into reusable quality tests.
What You Get
RAG Evaluation Report
Retrieval, grounding, citation and refusal failures with reproducible evidence and severity — white-label if you present it to your client.
Question and Evidence Set
A reusable evaluation dataset with expected evidence, edge cases and rephrasings, tailored to your knowledge base.
Calibrated Scoring Setup
Scoring rules checked against human judgement so results stay comparable between runs.
Reusable Test Coverage for Future Changes
Confirmed failures turned into tests your team reruns after prompt, model, chunking or content changes.
Discuss a RAG Evaluation Pilot
Start with one RAG system your team delivers and see what a specialist evaluation surfaces.
Discuss a RAG Evaluation Pilot