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beginner
~6 weeks · 3-4 hrs/week· 6 stages

Prompt Engineer

From decent prompts to production-grade prompting

You'll write prompts that are reliable, structured, and interview-ready, and you'll know when to reach for RAG or fine-tuning instead.

Who this is for

You use AI daily and want to master the craft — for work, side projects, or interviews.

Prerequisites
  • You've used ChatGPT/Claude for real tasks.
  • Comfort reading small code snippets helps but isn't required.
North-star project

Publish a small 'prompt cookbook' — 10 tested prompts for real tasks, each with format, structure, and a note on when they fail.

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  1. 1

    Stage 1: The anatomy of a great prompt

    Every advanced technique layers on top of the fundamentals. Get these solid.

    Deliverable · Rewrite 3 of your worst-performing prompts using role / task / context / format.
  2. 2

    Stage 2: Show, don't tell

    Few-shot examples steer models more reliably than a paragraph of instructions.

    Deliverable · Pick one task you do repeatedly. Build a 3-example few-shot template for it.
  3. 3

    Stage 3: Reasoning + structured output

    For anything a program (or another human) reads, you need chain-of-thought and reliable JSON.

    Deliverable · Write a prompt that returns valid JSON for a real task, and validate it with a schema.
  4. 4

    Stage 4: Under the hood

    Understanding tokens, context, and how the model actually reads your prompt makes debugging obvious.

    Deliverable · Estimate the token count of a prompt you use often. Trim 30% without losing quality.
  5. 5

    Stage 5: Iterate, evaluate, ship

    Great prompt engineers treat prompts like code: versioned, tested, and measured.

    Deliverable · Build a 10-example eval set for one prompt. Score two versions. Pick the winner with data.
  6. 6

    Stage 6: Know when NOT to prompt

    Some problems need RAG (facts) or fine-tuning (behavior). Knowing this saves months.

    Deliverable · Publish your prompt cookbook (v1): 10 real prompts, each with format, examples, and known limits.