Prompt engineering for developers

About this course

This course treats prompts not as chat messages but as part of a product: code that depends on a model's answer needs a predictable, checkable result. It solves the problem of turning a prompt from a random phrasing into a managed system component.

The course moves from request anatomy and slot-based phrasing through response schemas as contracts and in-code validation to context management and call observability. Projects run on prompts from your own product: clean up one prompt's phrasing slot by slot, build an output contract with measured reliability, rework a scenario's context with deletion testing.

Built for developers who already call models from code and want to cut down on invalid and unstable answers. You'll need programming experience and hands-on work with model APIs — this isn't a course for people new to development.

What you'll learn

• Break a prompt into slots: role, task, context, constraints, format
• Design a response schema as a contract and validate it in code
• Tell useful few-shot examples apart from harmful ones
• Build a set of examples as a prompt test suite
• Measure output quality without a human in the loop
• Guard a prompt against leaks and injected text
• Set up retries, graceful degradation, and a fallback path
• Track quality regressions when switching models

What you'll end up with

• A prompt from your product rewritten slot by slot, with both versions to compare
• An output contract with a response schema and measured reliability
• A prompt test suite built from real inputs
• A certificate on paid plans

Not for you if you're after a library of ready-made prompts to copy rather than an engineering approach to building them.