Prompt engineering, from productivity to coding agents

Prompt engineering is the craft of writing input a language model will reliably act on. At the beginner end, that means turning ChatGPT or Claude from a novelty into a productivity tool — drafting emails, summarizing meetings, translating a rough idea into a first draft. In the middle, it means the techniques power users reach for when consistency starts to matter: structured prompts, few-shot examples, chain-of-thought, prompt chaining. At the far end, it means the patterns developers use to drive coding agents like Claude Code and Cursor — where the prompt is a spec, and the model is writing the code that ships. This module walks the full arc, one lesson per layer, so a reader picks up the mental model on lesson 1 and lands with real advanced patterns by lesson 9.

A note on scope. This is the technique module — the how of writing a prompt. Two adjacent modules deepen it in different directions. Context engineering argues that the inputs to the prompt (instructions, retrieval, memory, tool state) are the real quality lever at scale — prompt engineering is one layer inside that picture. TPM for AI products covers how you evaluate whether a prompt works. This module stays on the craft itself: what to write, why, and how it fails.

The knowledge graph

Every lesson sits on one arc — from the person typing into a chat box, through the techniques that make outputs reliable, to the patterns that let coding agents ship production code:

Read it in three passes. Beginner: the mental model, the six parts of a prompt, and the everyday patterns that turn an LLM into a productivity tool. Intermediate: the techniques power users use to make outputs consistent — structure, worked examples, step-by-step reasoning, multi-step chains. Advanced: how prompts change shape when the model can act — call tools, run code, edit files — and the specific patterns of prompting inside coding agents.

The lessons

• What prompt engineering actually is — the mental model shift from "asking questions" to "engineering the input a probabilistic system needs."
• The anatomy of a prompt — role, task, context, format, examples, constraints: the six parts a good prompt names on purpose.
• Everyday productivity patterns — the handful of moves that get 80% of the value out of ChatGPT or Claude for real work.
• Structured prompting: XML, delimiters, scaffolds — what changes when consistency starts to matter and the prompt gets reused.
• Few-shot, chain-of-thought, and self-consistency — the classical techniques that reliably improve reasoning quality, and when each earns its cost.
• Prompt chaining and multi-step workflows — breaking a task into sub-prompts, passing outputs between steps, and why one prompt stops being enough.
• Prompting for tools and agents — how the prompt changes shape when the model can call tools, and the ReAct pattern that runs underneath most agent scaffolds.
• Prompting inside coding agents — how Claude Code, Cursor, and Aider prompts differ from chat prompts, and the "prompt as spec" pattern that runs modern agent-assisted development.
• When prompts fail: the diagnostic playbook — the short list of things that actually go wrong, and how to tell which one bit you.

Each lesson pairs the technique with a 🎯 For the AI PM (or coding-agent user) briefing — why it matters, the decision it changes, the question to ask your team or yourself, and the risk if ignored — plus one diagram that makes the mechanic concrete.

Connects to other tracks

• Context engineering — the strategic layer above prompt engineering: instructions, retrieval, memory, and tool state as the real quality lever, with the prompt as one component inside them.
• TPM for AI products — eval-driven development, the discipline that decides whether a prompt is good enough to ship.
• AI agents — the scaffolds this module's lesson 7 prompts drive.
• Agentic workflows — the multi-step orchestration patterns this module's lesson 6 chains fit inside.
• Tool calling — the function-schema mechanics this module's lesson 7 sits on top of.

📌 Close out the module: Recap & real-world examples.