LLM Evaluation Framework
Build an LLM evaluation framework with representative datasets, suitable scorers, release thresholds, human review, and production feedback.
Read guideAI quality engineering insights
Practical guidance for evaluating AI systems.
Use evidence-led frameworks for LLM evaluation, AI agent assurance, software testing, and safer release decisions.
Foundational guides
Build evaluation around the decision you need to make.
Each guide separates model, application, and agent-level evidence so teams can choose suitable cases, metrics, and release controls without relying on one generic score.
LLM Evaluation Metrics
Choose LLM evaluation metrics for correctness, groundedness, relevance, safety, latency, cost, and user outcomes without hiding risk in one score.
Read guideAI Agent Evaluation Framework
Evaluate AI agents across tasks, tools, retrieval, memory, safety, latency, cost, and downstream outcomes with a repeatable release framework.
Read guideFrom guidance to delivery
Need an independent evaluation?
QA-CS can translate product requirements, production risks, and known failures into a maintained evaluation strategy with traceable results and accountable human review.
- Representative datasets and task coverage
- Deterministic, rubric-based, and human scoring
- Regression evidence across meaningful change
- Explicit uncertainty, limitations, and residual risk
Start with clarity
Turn AI quality questions into testable evidence
Share the product, release, or operational challenge. We will help define the right next step.