Retrieval relevance
Whether the system retrieves the right documents, passages, and versions for the user's question.
RAG Testing
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.
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.
Focused coverage for teams that need evidence, not generic QA theater.
Whether the system retrieves the right documents, passages, and versions for the user's question.
Whether the final answer is supported by the retrieved or uploaded source material.
False citations, missing citations, irrelevant citations, and citations that do not support the claim.
Whether the product asks for clarification instead of inventing an answer when context is absent.
Large files, scanned documents, tables, mixed formats, duplicate sources, and conflicting documents.
Changes in retrieval behavior after index updates, chunking changes, model swaps, or prompt edits.
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.
Testing for fabricated answers, unsupported claims, and false confidence.
Testing for prompt adherence, hallucinations, refusals, and model drift.
Senior-led QA for LLM, agent, RAG, and AI workflow products.
Common questions before we scope the work.
Not always. Access helps diagnose root cause, but we can start from black-box product behavior if needed.
Yes. We can test against controlled internal documents, dummy data, or anonymized client material under NDA.
No. Hallucination is one output. RAG testing also covers retrieval relevance, citation accuracy, context assembly, and UX trust.
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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
We test AI products, LLM features, agents, RAG systems, and automation workflows the way real users interact with them.
infas@qualura.com