Production AI Systems

Who this series is for

This series is for engineers responsible for an AI feature that real users depend on. It assumes familiarity with LLM applications, RAG and agents. The topics are the ones that decide whether a feature survives its first month in production: knowing what happened on a bad request, keeping costs predictable, staying up when a provider has an outage, and not being trivially manipulated by user input.

What each chapter covers

• Observability & Tracing covers capturing prompts, retrieved context, tool calls, latency and cost per request, so any bad answer can be traced back to its cause.
• Cost & Reliability covers caching, model routing, retries with backoff, provider fallbacks and rate-limit handling.
• Context Engineering covers deciding what goes into the context window and in what order, which often matters more than the model you choose.
• Security & Guardrails covers prompt injection, data exfiltration through tools, input and output filtering, and least-privilege tool design.
• AI Safety, Bias & Content Moderation covers moderation pipelines and testing for harmful or biased outputs before users find them.

How to approach it

Apply each chapter to a system you already run, even a side project, and add one concrete control per chapter: tracing, then a cost budget, then a fallback, and so on. This is the longest series at about eleven hours, and the most valuable to revisit after an incident.