Outputs & retrieval
Validate AI outputs against explicit criteria. Evaluate RAG retrieval and answer quality with representative questions and failure cases.
A Proteus specialization
Your AI feature needs more than a convincing demo. Proteus helps validate outputs, test workflows, and catch regressions before the next release.
Discuss AI Workflow ReliabilityAI feature QA · RAG evaluation · Release validation
Quality beyond the happy path
A useful AI workflow has to hold up against the tasks, edge cases, and failures your users actually encounter.
We connect software QA with output evaluation: define acceptable behavior, build representative test cases, and make changes measurable.
Areas of focus
Validate AI outputs against explicit criteria. Evaluate RAG retrieval and answer quality with representative questions and failure cases.
Check end-to-end behavior, error handling, and integrations. Compare prompt, model, and workflow changes against a repeatable baseline.
Define release checks and monitoring that make quality changes visible, with clear ownership when a workflow falls short.
A clearer starting point
Bring the metric, funnel, or dashboard you’re unsure about. We’ll talk through what’s at stake and whether an audit is the right next step.
Book a Product Data Reliability Review30-minute conversation · No preparation required
Prefer email? hello@useproteus.io ↗