QANTUM LABS / QA & AI
QA for artificial intelligence you can evaluate
Artificial intelligence applications need quality criteria adapted to their behavior. We help define evaluations for LLMs, RAG systems and agent workflows using representative cases and reviewable evidence.
Talk to QAntum LabsFrom a convincing answer to acceptance criteria
We start with the specific use of AI: answering questions, retrieving information, classifying requests or performing a task. We define a correct outcome, what the system should refuse and when it needs to request help or acknowledge missing information.
In a document assistant, for example, a fluent answer may cite a policy absent from its sources. Evaluation should check the relationship between question, retrieved information and answer, as well as how the system communicates its limits.
Datasets and evaluation for LLMs and generative AI
We build evaluation sets from expected use, difficult cases and known failures. We agree criteria such as accuracy, source grounding, instruction compliance, latency and cost according to the project. We document conditions to compare model, prompt or retrieval changes.
We combine automated checks and human review when quality requires it. If another model acts as a judge, we check its criteria against examples assessed by people. Its score needs context and validation before it informs a release decision.
AI regression, agents and traceability
We record system versions, inputs, sources and results to investigate changes. For agents using tools, we also evaluate intermediate actions, permissions and goal completion. Tests should cover the full workflow and the points where it can recover from an error.
Evaluation can become part of CI/CD with agreed thresholds and review of critical cases. A release is assessed through observed behaviors and their limits, with a history that explains why the change was accepted.
Questions about QA and AI.
How does AI QA differ from traditional testing?
It shares risk analysis and acceptance criteria, but also needs to assess variable outputs and answers whose quality depends on context. Datasets, metrics, repeated testing where appropriate and professional review can be combined.
Can you evaluate a chatbot or RAG application?
We can agree an evaluation of its answers, sources, retrieval and refusal behaviors. The scope depends on its use, available data and the decisions you need to make about the system.
Related quality challenges
QA AI agents with clear tasks and reviewable results
A QA AI agent can help propose tests or investigate failures when its task is well defined. We design the workflow, its limits and how to measure its value for your QA professionals with you.
Superintelligence and QA: thinking about AI quality
Superintelligence describes a horizon of systems with general capabilities beyond human performance. Here we explore the questions that horizon brings to evaluation and quality engineering.
QA automation in CI/CD to inform every release
Continuous integration and continuous delivery need quality signals the team can interpret. We design an execution strategy that combines speed, coverage and useful failure evidence.
Bring this strategy to your project.
Tell us how your team works, which tools you use and which risks you need to address. We can discuss the scope of a consulting or evaluation engagement together.
Talk to QAntum Labs