evaluate-ai-changeslisted

# Evaluate AI Changes Treat model behavior as a versioned, probabilistic product surface rather than a single passing example. ## Establish Authority and Objectives 1. Define user-visible quality, safety, latency, reliability, and cost objectives before changing prompts, models, retrieval, tools, or policies. 2. Identify material risks, affected users, decision owners, and the offline-versus-production boundary. Use `plan-first` when rollout, contracts, or data choices require human decisions. 3. Confirm dataset provenance, consent, minimization, redaction, retention, residency, and provider data policy. Do not send private or production data to a model or tool without explicit authorization. ## Build the Evaluation 1. Freeze a representative, versioned dataset covering normal, edge, adversarial, multilingual, refusal, and previously failed cases. Keep holdout cases separate from prompt development. 2. Capture the current prompt, model, parameters, retrieval configuration, tool schemas, and routing logic as the baseline. 3. Test task quality plus relevant failure modes: hallucination, unsupported claims, prompt injection, data leakage, unsafe compliance, over-refusal, structured-output validity, tool selection and arguments, retrieval relevance, citation support, and fallback behavior. 4. Use deterministic unit and contract tests for adapters, parsers, schemas, permissions, and tool execution through `tdd-loop`. Do not use model scores as a substitute for code tests. 5.