Advanced Prompt Engineering
Master the art of crafting effective prompts to generate exactly the code you need for complex projects.
What You'll Learn
Description
Most disappointing AI output traces back to a vague request, not a weak model. This tutorial treats the prompt as the real interface to a coding LLM: you'll learn why structure beats length, and how a literal-minded model fills every gap you leave with its own defaults.
Each chapter pairs a technique with side-by-side before/after prompts you can copy and adapt — role priming, explicit constraints, pinned output formats, few-shot examples, chain-of-thought planning, pasting real context, acceptance criteria, and anti-hallucination guards. A short practice exercise at the end of every chapter lets you rewrite a weak prompt and compare it against a worked sample answer.
By the end you'll write prompts that specify the role, the rules, and the shape of the answer up front — so the first response is closer to mergeable and you spend far less time re-prompting.
What's Inside
- 1.Prompting Mindset & Intent — Why prompt structure beats prompt length, and the mental model of an LLM as a literal-minded pair programmer.
- 2.Role, Constraints & Output — Role/persona priming, explicit constraints, and pinning the exact output format you want back.
- 3.Few-Shot & Chain-of-Thought — Few-shot examples, chain-of-thought step decomposition, and feeding real context (files, errors, types).
- 4.Evals & Anti-Hallucination — Acceptance criteria and evals, iterative refinement, and guarding against hallucinated APIs.
