RAG Testing

Retrieval is not enough. The answer still has to be grounded.

Qualura tests retrieval-augmented generation systems for grounding, citation quality, retrieval relevance, missing-context handling, hallucination, and product-level reliability. We check whether the system actually uses the right source material, not whether it merely returns an answer.

RAG failures are often quiet

A RAG product can retrieve the wrong passage, cite the right document while answering from memory, ignore an uploaded file, or generate a confident answer when the source does not support it.

We test the full chain: query understanding, retrieval, ranking, context injection, answer generation, citation behavior, UI presentation, and user recovery when the answer is uncertain.

RAG quality areas

Focused coverage for teams that need evidence, not generic QA theater.

Retrieval relevance

Whether the system retrieves the right documents, passages, and versions for the user's question.

Grounding validation

Whether the final answer is supported by the retrieved or uploaded source material.

Citation accuracy

False citations, missing citations, irrelevant citations, and citations that do not support the claim.

Missing-context handling

Whether the product asks for clarification instead of inventing an answer when context is absent.

Document edge cases

Large files, scanned documents, tables, mixed formats, duplicate sources, and conflicting documents.

Regression and drift

Changes in retrieval behavior after index updates, chunking changes, model swaps, or prompt edits.

How RAG testing works

We build test questions from real user goals, not only from neat demo examples.

We test relevant, irrelevant, missing, conflicting, and partially correct source contexts.

We report whether failures came from retrieval, ranking, context assembly, generation, citation behavior, or product UX.

What you get

  • Retrieval quality findings
  • Grounding validation report
  • Citation accuracy issues
  • Missing-context failure cases
  • Document handling defects
  • RAG remediation roadmap

Related services

AI QA Agency

Senior-led QA for LLM, agent, RAG, and AI workflow products.

FAQ

Common questions before we scope the work.

Do you need access to the vector database?

Not always. Access helps diagnose root cause, but we can start from black-box product behavior if needed.

Can you test private document workflows?

Yes. We can test against controlled internal documents, dummy data, or anonymized client material under NDA.

Is RAG testing only about hallucinations?

No. Hallucination is one output. RAG testing also covers retrieval relevance, citation accuracy, context assembly, and UX trust.

Work With Us

Need AI testing before your product ships?

Book a 30-minute discovery call. We will understand your product, identify the riskiest AI surfaces, and recommend whether a sprint or custom engagement fits best.

Qualura

Senior-led. Evidence-first. NDA-bound.

We test AI products, LLM features, agents, RAG systems, and automation workflows the way real users interact with them.

infas@qualura.com