EngineeringReasoningMedium complexity
LLM Application Evaluation
Assistants and agents that looked good in a demo degrade quietly in production when prompts, data or the model change.
How we approach it
Build an evaluation set from real questions with checked answers. Score every release on accuracy, sourcing and refusals, using automated judges calibrated against human reviewers. Monitor production traces, sample them for review, and block any release that falls below the threshold.
Business value
Quality that is measured, not assumed
Technology stack
- Evaluation harness
- LLM-as-judge
- tracing
- human review queue
- CI integration
Related service
Infrastructure & MLOpsFurther reading
Related use cases
- Cross-Industry · EngineeringModel Retirement & Provider Exit PlanModel providers retire versions at short notice and change prices and terms, and applications tuned to one model break when it goes.
- Cross-Industry · EngineeringExpert-Validated Knowledge BaseKnowledge bases built by crawling go stale and fill with pages that are not what they seem, and the assistant on top repeats the errors.
- Cross-Industry · EngineeringFine-Tuned Domain ModelsDecide when a small model tuned on your own data beats a large general model, and when retrieval alone is enough.