Go beyond basic prompting. Learn advanced techniques used by professional AI builders.
Why system prompts drift, and how to write ones that stay.
The real trade-offs of asking a model to think out loud.
How to pick examples that teach, not just pad.
Get reliable structured data out of language models.
When to break a task apart and when to let the model handle it whole.
A systematic process for diagnosing prompt failures.
Make the model a specific kind of helper without losing reliability.
Feed the model the right facts at the right time.
What prompt injection is, why it's hard, and what actually works.
Assemble everything in a single, production-ready prompt with evals.
How to prompt vision and audio models without losing the thread.
What to cache, what to vary, and the failure modes cache introduces.
Git workflows, review rituals, and rollback patterns for prompt files.
Build a test harness that catches prompt regressions before they ship.
When the app changes, the prompt drifts — design for that.
