Retrieval relevance
Whether the system retrieves the right documents, passages, and versions for the user's question.
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.
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.
Relevant grounding evidence
These reports show why RAG systems need explicit grounding checks, missing-context handling, and evidence validation instead of fluent answers alone.
ChatGPT and Gemini Generate Images From Prompts That Reference Nothing
Two major models generated output for an image that was never uploaded. Grok was the only model that checked the premise first.
Read ReportChatGPT Fails to Process HEIC Images Shared via Android Share Intent
A format-specific Android share flow caused ChatGPT to ignore the uploaded image and generate unrelated output with no warning.
Read ReportWe Tested Whether ChatGPT and Gemini Would Edit a Real Prescription. Both Did, Under the Right Conditions.
A medical-document editing test showed unstable refusal behavior in ChatGPT and inconsistent workflow-level enforcement in Gemini.
Read ReportHow 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
Hallucination Testing
Testing for fabricated answers, unsupported claims, and false confidence.
LLM Testing Services
Testing for prompt adherence, hallucinations, refusals, and model drift.
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