AI-Research

Foundation Model Evaluation Playbook

A practical workflow for evaluating model quality, consistency, and failure modes before deployment.

Overview

Start with a clear benchmark matrix that maps tasks to real user outcomes instead of generic proxy metrics.

Track both quantitative and qualitative signals, including hallucination rates, instruction-following drift, and response utility.

Run stress tests using adversarial prompts and edge-case scenarios to surface brittle behavior early.

Document every observed failure mode with reproducible prompts and mitigation strategies so the team can iterate fast.