Unsupported claims
Answers that include facts, numbers, names, dates, or conclusions not supported by the available context.
Hallucination Testing
Fluent output is not the same as correct output.
Qualura tests AI products for hallucinations, unsupported claims, fabricated citations, missing-context assumptions, and false confidence. We focus on where hallucination becomes product risk: user decisions, enterprise trust, compliance exposure, and workflow failure.
Hallucinations are not always obvious
The easiest hallucinations to catch are absurd. The dangerous ones are plausible, specific, and confidently presented in contexts where the user expects precision.
We test whether answers are grounded in available evidence, whether citations support the claim, whether the product admits uncertainty, and whether missing information triggers clarification instead of invention.
Hallucination test areas
Focused coverage for teams that need evidence, not generic QA theater.
False citations
Citations that do not exist, do not support the claim, or point to irrelevant source material.
Missing input handling
Whether the model proceeds when a referenced file, image, document, or source is absent.
Confidence calibration
Whether the product communicates uncertainty instead of presenting weak evidence as fact.
RAG grounding
Whether generated answers stay within retrieved or uploaded source material.
High-stakes contexts
Medical, legal, financial, compliance, hiring, education, and operational workflows where false confidence is costly.
How we test hallucination risk
We create prompts and workflows where the correct behavior is to answer, ask for missing context, cite sources, or refuse to guess.
We compare outputs against available context, retrieved documents, uploaded files, and the product's stated behavior.
We report hallucinations with exact prompts, screenshots, source comparison, and the user impact of accepting the answer.
What you get
- Hallucination risk report
- Unsupported claim examples
- Citation accuracy findings
- Missing-context failure cases
- Grounding recommendations
- User trust impact assessment
Related services
RAG Testing
Grounding, retrieval, citation, and answer-quality testing for RAG systems.
LLM Testing Services
Testing for prompt adherence, hallucinations, refusals, and model drift.
AI Safety Testing
Safety, abuse, refusal, and harmful-output testing for AI products.
FAQ
Common questions before we scope the work.
Can hallucinations be eliminated completely?
No. The practical goal is to reduce hallucination risk, surface uncertainty, improve grounding, and prevent high-impact false answers from reaching users.
Do you test hallucinations in RAG systems?
Yes. RAG systems can still hallucinate through poor retrieval, weak grounding, bad citation behavior, or missing-context assumptions.
How do you prove a hallucination?
We compare the output against the available evidence and document where the claim is unsupported, contradicted, or invented.
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