Prompt Engineering

The craft of writing inputs that reliably get the outputs you want

What it is

Prompt engineering is the practice of designing model inputs to elicit reliable, high-quality outputs. It encompasses system prompt design, few-shot examples, chain-of-thought instructions, output format specification, and iterative refinement based on observed failures.

Key techniques: zero-shot prompting (just ask), few-shot prompting (provide examples of input-output pairs), chain-of-thought (instruct the model to reason step-by-step before answering), role prompting (assign a persona or expertise), and output structuring (specify JSON, markdown, or other formats).

Prompt engineering is less of a science and more of a craft, different models respond differently to the same prompts, and prompts that work for GPT-4 may not work for Claude.

Why it matters

This is the most immediately applicable skill for any work with AI models. Whether you're building a product, running an experiment, or helping a client, your ability to write effective prompts determines whether the AI actually does what you want. It's one of the fastest skills to improve with deliberate practice, ship something and iterate on the prompts.