Working with LLM APIs: advanced

About this course

This course turns a language-model call from a script that sometimes breaks into a reliable production component. It covers the engineering side of LLM APIs: messages and roles, sampling parameters, tokens, error codes, retries, idempotency, and rate limits — everything standing between "works in a demo" and "works under load."

The five modules move from how a call is structured to reliability, response shape, cost and speed, and production readiness. In "Reliability" you build a wrapper with errors classified into four reactions, backed by measurements. In "Response shape" you build a schema contract with enums and no extra fields. In "Cost and speed" you budget latency and spend and prove a measurable improvement.

For people already calling LLM APIs in code who want to stop guessing why it breaks. You need programming experience (any language; examples lean Python-like) and basic REST/HTTP familiarity. There's no general-awareness filler here — just working through a real call.

What you'll learn

• Log the messages array with roles and token counts for debugging a call
• Classify API errors into reactions and build a retry wrapper with exponential backoff
• Guard calls against double billing with idempotency
• Design structured output against a JSON schema with enums and constraints
• Wire up tool calls and handle their arguments and errors
• Estimate call cost upfront and shrink context without losing quality
• Set up caching and parallel or batch calls for speed
• Prepare an integration for production: configuration, observability, tests, and security

What you'll end up with

• A working reliability wrapper around a model call, backed by measurements
• A structured-output contract with a schema for one real task
• A cost/latency budget with a proven measurable improvement on both axes
• An operational document for your model integration (paid tiers include reviewer feedback and a certificate)

Not for people looking for an AI intro or ready-made prompts without touching code — you'll be writing and fixing API calls yourself.