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Shipping AI Features to Production

Free

Learn the engineering practices that separate AI features that work reliably in production from ones that become liabilities. This course is for software engineers and product engineers who are building LLM-powered features and want to ship them with confidence. It assumes familiarity with software development and basic LLM API usage. No ML background required. You will learn how to design production-grade prompts, build evaluation systems, set up observability and cost controls, handle failures gracefully, and deploy AI features with the same discipline you apply to any critical piece of infrastructure.

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Complete lessons in order to unlock the next — structured progression.

What Makes AI Features Different in Production

Understand the unique failure modes of AI features, why they require different engineering practices, and how to use the production readiness framework before you write a line of eval code.

  1. 1What Makes Ai Features Different In ProductionTutorial
  2. 2A Production Readiness Framework For Ai FeaturesTutorial
  3. 3Production Ai Foundations CheckQuiz

Prompt Engineering for Production

Write prompts that are stable, testable, and secure at scale. Learn prompt structure, output format specification, temperature selection, defensive prompting, and version control practices for production prompts.

  1. 4Prompt Engineering For ProductionTutorial
  2. 5Versioning And Managing Prompts In ProductionTutorial
  3. 6Prompt Injection: Risks And DefensesTutorial
  4. 7Prompt Engineering For Production CheckQuiz

Testing and Evaluation for AI Features

Build an evaluation system that catches regressions before they reach production. Learn the four layers of AI feature testing, how to build and maintain eval datasets, and how to calibrate and use LLM-as-judge evaluation.

  1. 8Testing Ai Features: Strategies And PatternsTutorial
  2. 9Building And Maintaining Eval DatasetsTutorial
  3. 10Llm As Judge: Automated Quality EvaluationTutorial
  4. 11Testing And Evaluation CheckQuiz

Observability, Monitoring, and Cost Management

Build visibility into your AI features so problems are caught before users report them. Learn what to log for every LLM call, how to set up alerts for latency, errors, and silent drift, and how to manage token costs systematically.

  1. 12Observability For Llm ApplicationsTutorial
  2. 13Monitoring And Alerting For Ai FeaturesTutorial
  3. 14Token Cost Optimization For Production Ai FeaturesTutorial
  4. 15Observability, Monitoring, And Cost CheckQuiz

Deployment, Reliability, and Shipping Responsibly

Ship AI features safely with feature flags, canary releases, and eval-gated CI/CD pipelines. Build the reliability patterns that protect users when things go wrong: timeouts, retries, circuit breakers, and graceful degradation. Complete the capstone project.

  1. 16Deployment Strategies For Ai FeaturesTutorial
  2. 17Reliability Engineering For Ai FeaturesTutorial
  3. 18Shipping Ai Features To Production: Capstone ProjectTutorial
  4. 19Deployment And Reliability CheckQuiz

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