AI Operations and Quality Readiness Checklist

Description

AI Operations and Quality Readiness Checklist
73 actionable checkboxes for AI observability, hallucination management, CI/CD pipelines, MLOps, and prompt registry governance.

This role-based checklist contains 73 ready-to-use checkboxes extracted from the LLM Production Readiness — Complete Checklist (v8 consolidated). It covers the operational infrastructure, quality assurance, and continuous improvement systems required to run LLMs reliably in production.

What’s Inside:

* 73 checkboxes across 6 domains: Observability (22), Hallucination Management (14), User Feedback (4), CI/CD (13), MLOps (9), Prompt Registry (11)
* Core telemetry: full LLM call instrumentation (prompt, response, tokens, latency, session, model version, cost), OpenTelemetry-compatible tracing, P50/P95/P99 latency tracking, cost attribution per user/team/model, RAG pipeline tracing with document relevance scores, and token consumption monitoring
* Quality monitoring: LLM-as-judge for hallucination and faithfulness scoring, drift detection with baseline divergence alerting, prompt version A/B testing with statistical significance gating, and toxicity/bias/harmful content rate monitoring
* Continuous & online evaluation: automatic production traffic sampling (100% for high-risk, 10-20% for standard), continuous improvement loop (eval failures → labeled datasets → next iteration), automated eval score alerts (1-hour page, 24-hour incident), moving average trend tracking, and champion/challenger framework
* Alerting & incident response: hallucination rate spike, latency degradation, cost anomaly, error rate threshold, negative feedback spike, and eval score drop alerts integrated into Slack/PagerDuty/Teams with escalation paths and on-call rotation
* Out-of-distribution input detection: model confidence/uncertainty monitoring and per-use-case confidence thresholds with abstention or human escalation
* Automated rollback on observability signals: canary failure, error rate breach, hallucination budget exceeded, groundedness score drop
* Hallucination budget & verification loop: per-use-case hallucination budget with rollback trigger, LLM as generator inside verification loop (not oracle), multi-model consensus for high-stakes outputs, explicit abstention testing, and per-use-case/task-type hallucination rate tracking
* Bias & fairness monitoring: pre-launch bias evaluation criteria, stratified test set evaluation before every release, non-English performance degradation testing, adversarial fairness probes, and continuous production fairness monitoring
* Cost alerting & budget controls: per-user token budget ceilings, tiered daily spend alerts (70%/90%/100%), cost-per-query trend tracking, and automated token budget ceiling for agentic tasks
* User feedback loop: explicit feedback instrumentation (thumbs up/down, task completion, conversation abandonment), systematic feedback-to-training loop, negative feedback spike alerting, and periodic stratified expert review
* Deployment pipeline: CI/CD with security scanning and quality gates, mandatory canary periods, shadow mode for high-risk changes, explicit promotion criteria (shadow → canary → 25% → 100%), automated rollback on quality regression, and model artifact registry with checksums
* Three-tier CI evaluation: Tier 1 deterministic assertions (every PR, near-zero cost), Tier 2 LLM-as-judge on golden eval set (every PR, moderate cost), Tier 3 stratified human sampling (major releases)
* Prompt brittleness testing, golden evaluation dataset (minimum 50-200 inputs), hallucination score CI gate, and experiment tracking
* Training dataset version control: dataset registry or DVC, model-to-dataset version tagging, dataset changelog, pre-training snapshot hash verification, and immutable object storage (S3 Object Lock/GCS Object Hold)
* Performance optimisation: inference bottleneck profiling (TTFT, ITL, throughput, KV-cache utilisation), query caching at gateway layer, async writing for outlet/learning pipelines, and quantisation quality validation on domain-specific golden eval set
* Prompt registry & model pinning: all prompts extracted to versioned registry (no hardcoded prompts), prompt immutability (changes create new versions), hot-fix capability without full redeploy, model pinning to specific snapshot IDs (not floating aliases), model upgrade treated as release with full eval, provider deprecation notice subscription, model migration runbook
* Sampling parameter governance: explicit per-use-case settings for temperature/top-p/max_tokens/stop_sequences, task-type temperature guidelines (0.0 extraction, 0.3-0.5 summarisation, 0.7-0.9 creative), seed parameter pinning for audit trails, and parameter versioning alongside prompt text
* Interactive HTML with progress tracking — check off items as you complete them

Use Cases:

* Production AI observability stack design with full telemetry instrumentation
* Hallucination budget definition, verification loop architecture, and multi-model consensus
* CI/CD pipeline design with three-tier evaluation gates and automated rollback
* MLOps dataset versioning, model lifecycle management, and inference optimisation
* Prompt registry governance, model version pinning, and sampling parameter control
* Bias and fairness monitoring with stratified evaluation and adversarial probes

Perfect For: SREs, platform engineers, MLOps engineers, QA leads, DevOps teams, and operations managers responsible for running AI systems reliably in production.