Raymond Clanan

Production AI Systems Checklist

Free downloadable checklist for building production-ready AI/ML systems. Covers architecture, observability, cost optimization, and deployment best practices.

Building production AI systems is different from prototyping. This checklist covers everything you need to deploy AI/ML features that are fast, observable, and reliable.

What's inside

This checklist covers 8 critical areas for production AI systems:

1. Architecture & Design

  • Multi-provider abstraction: Don't lock into single LLM provider
  • Fallback strategies: Handle provider outages gracefully
  • Rate limiting: Protect against runaway costs
  • Caching layer: Reduce API calls and improve latency
  • Async/streaming: Don't block user experience
  • Error handling: Graceful degradation when LLMs fail
  • Idempotency: Safe retries without duplicate side effects

2. Cost Optimization

  • Token counting: Accurate cost estimation before requests
  • Budget enforcement: Hard limits to prevent overruns
  • Intelligent routing: Choose cheapest provider for task
  • Response caching: Avoid duplicate expensive calls
  • Prompt optimization: Shorter prompts = lower costs
  • Model selection: Use smallest model that works
  • Cost monitoring: Real-time dashboards and alerts

3. Observability

  • Structured logging: JSON logs with context
  • Distributed tracing: Track requests across services (OpenTelemetry)
  • Metrics collection: Latency, cost, tokens, errors (Prometheus)
  • Dashboards: Grafana views for key metrics
  • Alerting: Get notified before users complain
  • Request/response logging: Debug production issues
  • Performance profiling: Identify bottlenecks

4. Performance

  • Latency targets: Define acceptable response times (p95, p99)
  • Streaming responses: Show results as they arrive
  • Connection pooling: Reuse HTTP connections
  • Parallel requests: Fan-out when possible
  • CDN for static content: Faster asset delivery
  • Database query optimization: Fast metadata lookups
  • Load testing: Simulate production traffic

5. Security & Safety

  • Input validation: Sanitize user prompts
  • Output filtering: Block harmful content
  • PII detection: Don't send sensitive data to LLMs
  • API key rotation: Regular credential updates
  • Access controls: Who can use which features
  • Audit logging: Track all LLM interactions
  • Content moderation: Filter inappropriate outputs

6. Quality & Evaluation

  • Ground truth dataset: Known good inputs/outputs
  • Offline evaluation: Test before deploying
  • A/B testing framework: Compare prompt variations
  • User feedback collection: Thumbs up/down
  • Accuracy metrics: Measure answer quality
  • Regression testing: Ensure changes don't break things
  • Hallucination detection: Catch made-up facts

7. Deployment & Operations

  • CI/CD pipeline: Automated testing and deployment
  • Blue/green deployments: Zero-downtime updates
  • Feature flags: Gradual rollouts
  • Rollback strategy: Quick revert if needed
  • Health checks: Monitor service availability
  • Dependency management: Track library versions
  • Documentation: Runbooks for common issues

8. RAG-Specific (If Applicable)

  • Vector database: Efficient similarity search (pgvector, Pinecone, etc.)
  • Embedding model selection: Fast + accurate
  • Chunking strategy: Optimal document splitting
  • Re-ranking: Improve relevance of retrieved docs
  • Citation tracking: Link outputs to sources
  • Freshness controls: Keep knowledge up-to-date
  • Hybrid search: Combine vector + keyword search

Common pitfalls to avoid

Don't do this

  1. No observability: "It works on my machine" isn't good enough
  2. Ignoring costs: $10K AWS bill surprise
  3. No fallbacks: Single provider outage breaks your app
  4. Blocking UX: User waits 10 seconds staring at spinner
  5. No caching: Paying for identical requests repeatedly
  6. Ignoring security: Leaking PII to external LLM APIs
  7. No evaluation: Hope and pray deployment strategy
  8. Hard-coded prompts: Can't iterate without code changes

Do this instead

  1. Logs, traces, metrics from day one
  2. Budget limits and alerts before going to production
  3. Multi-provider routing with automatic failover
  4. Streaming responses for better perceived performance
  5. Intelligent caching at multiple levels
  6. PII detection and filtering before external calls
  7. Offline evaluation pipeline with ground truth
  8. Prompt version control with A/B testing framework

Real-world examples

Example 1: Cost Explosion

Problem: Startup deployed RAG system, got $47K bill in first month.

Root Cause:

  • No caching (repeated identical queries)
  • Using GPT-4 for everything (even simple tasks)
  • No rate limiting (one user made 10K requests)

Fix:

  • Added response cache (40% cost reduction)
  • Intelligent model routing (GPT-3.5 for simple queries)
  • Per-user rate limits

Result: Reduced to $8K/month

Example 2: Production Outage

Problem: OpenAI API outage took down entire product.

Root Cause:

  • No fallback provider
  • No graceful degradation
  • Synchronous blocking calls

Fix:

  • Added Anthropic as fallback
  • Show cached results during outages
  • Async processing with queue

Result: 99.9% uptime despite provider issues

Example 3: Poor Quality

Problem: Users complained about incorrect answers, no way to measure.

Root Cause:

  • No evaluation framework
  • No user feedback mechanism
  • No way to track accuracy over time

Fix:

  • Built ground truth test set (500 Q&A pairs)
  • Added thumbs up/down buttons
  • Automated daily evaluation

Result: Improved accuracy from 62% to 87%

Next steps

Need help implementing?

If you're building production AI systems and want expert guidance:

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About the author

I'm Raymond, and I've spent 20+ years building production systems, including AI/ML platforms for enterprise clients. I've helped companies:

  • Reduce LLM costs by 30-60%
  • Improve response times by 40-80%
  • Achieve 85%+ accuracy in RAG systems
  • Ship AI features that scale

Learn more about me or hire me to help with your AI project.