Production Prompt and Configuration Drift Audit
Reconcile approved AI prompts and runtime configuration with deployed variants, trace unauthorized drift, and define rollback, adoption, or revalidation decisions.
Category
Prompt Engineering
Prompts for designing, testing, improving, and hardening reusable prompt systems.
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Regression Acceptance Threshold Calibration
Calibrate risk-based regression thresholds using measurement error, baseline variance, slice exposure, practical significance, and explicit release trade-offs.
Adversarial Evaluation Coverage Review
Map credible abuse and failure hypotheses to adversarial tests, exposed system surfaces, production controls, and release-blocking coverage gaps.
Production Feedback-to-Evaluation Gap Analysis
Trace incidents, reviewer corrections, user feedback, and operational failures into evaluation coverage, exposing missing, stale, or misweighted regression evidence.
Evaluation Metric Gaming and Proxy Failure Audit
Determine whether optimization against an AI evaluation metric rewards undesirable behavior, hides target failure, or distorts release and operating decisions.
Evaluation Dataset Coverage and Contamination Audit
Audit an evaluation dataset for provenance, coverage, leakage, contamination, duplication, label quality, and admissibility before it supports release claims.
Judge-Model Reliability and Calibration Review
Evaluate whether a proposed automated judge is calibrated enough for a specific scoring or classification decision.
Production AI Evaluation Drift Detection Plan
Design a control plan that detects when a once-approved AI evaluation is no longer reliable for production decisions.
MCP Connector Permission and Data-Boundary Review
Assess an MCP connection’s capabilities, authorization, token handling, data flows, user consent, side effects, and reapproval controls before enabling or renewing access.
AI Model Upgrade Regression and Rollback Readiness Pack
Evaluate a candidate AI model against the current production model and produce evidence-based release, canary, monitoring, and rollback decisions.
AI Feature Abuse Case Red-Team Workshop
Run a structured AI safety red-team workshop to identify abuse cases, assess safeguards, define monitoring, and prepare launch-readiness decisions.
Team AI Usage SOP and Review Loop
Create a practical SOP for responsible team AI use, covering allowed use cases, restricted data, review rules, approval roles, escalation paths, and update cadence.