Evaluate AI Apps: Know Quality Before Users Do
AI Evaluation & Reliability: leave with a labelled eval set, automated metrics, a calibrated judge check and a regression gate in CI.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why ai evaluation & reliability matters — the common problem: AI changes ship on vibes, and regressions are discovered by users.
- 2Core concepts in plain language: Evaluation datasets, reference answers, task-specific metrics, retrieval quality, factuality, groundedness
- 3Going further: tool-call correctness, human evaluation, judge-model calibration, regression testing, adversarial testing, quality–cost–latency trade-offs
- 4Framework walkthrough: NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, Model Cards
- 5Practical workflow, built live: A labelled eval set, automated metrics, a calibrated judge check and a regression gate in CI.
- 6How to measure it: Regression-eval pass rate, Drift alerts, Security findings, Incident count
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A labelled eval set, automated metrics, a calibrated judge check and a regression gate in CI.
- A working understanding of NIST AI Risk Management Framework and OWASP Top 10 for LLM Applications
- A short list of measures to track: Regression-eval pass rate, Drift alerts, Security findings
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