What is prompt engineering?
Prompt engineering is crafting the input to an LLM so it reliably produces the output you want. Clear instructions, examples, explicit constraints, and a defined output format turn a vague, inconsistent response into a dependable one. It is the most immediately useful LLM skill.
Why it matters
The same model gives wildly different results depending on how you ask. Good prompting is often the difference between a feature that works and one that breaks unpredictably — without any model change. It is the cheapest, fastest lever you have, and a baseline skill for building on LLMs.
What to learn
- Clear, specific instructions
- Few-shot prompting with examples
- Specifying the output format
- System prompts and roles
- Chain-of-thought for reasoning tasks
- Giving the model context to work from
- Iterating and testing prompts systematically
Common pitfall
Treating prompting as one lucky guess instead of an iterative, testable process. A prompt that works once can fail on the next input. Build a small set of test cases, refine the prompt against them, and pin down the output format — reliable prompting comes from iteration, not from a single magic phrase.
Resources
Primary (free):
- OpenAI — Prompt engineering guide · docs
- Anthropic — Prompt engineering · docs
- Learn Prompting · docs
Practice
Take a task an LLM does inconsistently and improve it: add a clear instruction, one or two examples, and an explicit output format. Test the prompt on several inputs and refine until it is reliable. Done when the same prompt produces consistent, correctly formatted output across your test cases.
Outcomes
- Write clear instructions with examples and constraints.
- Specify and enforce an output format.
- Use chain-of-thought for reasoning tasks.
- Refine prompts iteratively against test cases.