Prompt adherence
Explicit instructions, formatting constraints, role boundaries, output requirements, and conflicting user requests.
LLM Testing Services
Find the model behavior problems before users normalize them.
Qualura tests LLM-powered products for prompt adherence, hallucinations, unsafe responses, context loss, refusal quality, regression drift, and real-world interaction failures. The goal is not to prove the model can answer once. It is to understand how it behaves across pressure, variation, and edge cases.
LLM testing has to measure behavior, not only availability
A passing API response is not the same as a correct product response. LLMs can produce fluent output while ignoring the uploaded file, inventing missing facts, over-refusing safe requests, or acting on assumptions that were never true.
We test the product around the model: prompts, context windows, retrieval, UI state, file handling, retry paths, and the way the system communicates uncertainty to the user.
LLM failure modes we cover
Focused coverage for teams that need evidence, not generic QA theater.
Hallucination and false confidence
Unsupported claims, fabricated details, false citations, and confident answers without evidence.
Context and memory
Long conversations, edited prompts, uploaded files, prior turns, and state carryover across sessions.
Refusal quality
Over-refusals, under-refusals, inconsistent safety handling, and poor explanations for blocked requests.
Regression drift
Behavior changes across releases, model switches, prompt edits, and retrieval changes.
Product integration
How model output affects downstream workflows, UI states, decisions, and user trust.
How LLM testing is scoped
We identify the highest-risk user journeys and the prompts or workflows most likely to create trust damage.
We run controlled variations across realistic prompts, adversarial prompts, missing-context prompts, long-context prompts, and repeated attempts.
We report reproducible findings with exact prompts, observed outputs, environment details, and recommended product-level fixes.
What you get
- LLM behavior test matrix
- Prompt adherence findings
- Hallucination and grounding report
- Safety and refusal analysis
- Regression-risk recommendations
- Evidence-backed defect list
Related services
Hallucination Testing
Testing for fabricated answers, unsupported claims, and false confidence.
RAG Testing
Grounding, retrieval, citation, and answer-quality testing for RAG systems.
Prompt Injection Testing
Adversarial testing for prompt injection, data leakage, and tool misuse.
FAQ
Common questions before we scope the work.
Do you test model quality or product quality?
Both, but always through the product lens. A raw model may behave one way while the product prompt, retrieval layer, UI, or workflow creates a different failure.
Can you test multiple models?
Yes. We can compare behavior across models when your product supports model switching or when you need benchmark-style evidence.
Do you need access to our prompts?
Access helps, but it is not always required. We can also test externally from the user experience if prompt access is unavailable.
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