AI engineering learning path
Models & APIs
Understand model limits, build a reliable interface, and choose with evidence.
Suggested background: basic Python, HTTP, and JSON. The examples focus on engineering boundaries and can be practiced locally.
From making a call to making a decision.
From making a call to making a decision.
Chapters
Understand context before your first model call
Separate tokens, context windows, and output budgets, then practice assembling model input with an offline example.
Streaming and structured output solve different problems
Separate incremental display from a complete business result and validate output with a local Python example.
Build a task evaluation set before choosing a model
Define success with actual tasks and compare quality, latency, and failure modes rather than a leaderboard alone.