Prompt engineering: techniques, examples and templates
Prompt engineering is the practice of writing and testing instructions so an AI model reliably produces the output you need. It covers how to state the task, give context and examples, set the format and check results. This section teaches the core techniques, including system prompts, few-shot prompting and chain-of-thought, with examples you can adapt to any assistant.
A model can only work with what it is given. Vague requests get generic answers; a clear task, the right context, a few examples and an explicit output format usually get far better ones. Prompt engineering is the discipline of making those choices deliberately and checking whether they work, rather than rewording a request until something looks right.
The core techniques are well documented. Few-shot prompting, described by Brown et al. in 2020, shows the model worked examples inside the prompt. Chain-of-thought prompting, described by Wei et al. in 2022, asks for intermediate reasoning steps. System prompts set standing rules for a whole conversation, and sampling settings such as temperature trade consistency for variety.
Good prompts are tested, not guessed. This section shows how to build a small set of test inputs, compare prompt versions side by side and keep a library of prompts that work for your team. The same principles apply across ChatGPT, Claude, Gemini and other assistants, although each model responds a little differently.
What you will learn
- The anatomy of a strong prompt: task, context, examples, format and constraints
- When to use system prompts, few-shot examples and chain-of-thought
- How temperature and other settings change output
- How to test and compare prompts instead of guessing
- Reusable templates for writing, analysis and coding tasks
Beginner lessons
What is temperature in AI models?
Temperature is a setting that controls how much randomness an AI model uses when it picks each next word. At a low temperature the model…
What is a prompt?
A prompt is the text you give an AI model to tell it what you want: a question, an instruction, or a request plus any context, examples or…
Guides in this topic
Key terms
- Prompt
A prompt is the input you give an AI model to tell it what to do: a question, an instruction, context, examples or a file. The model generates its…
- System prompt
A system prompt is a set of instructions given to an AI model before the conversation begins, defining its role, tone, rules and output format for…
- Few-shot prompting
Few-shot prompting is a technique in which you include a small number of worked examples of a task in the prompt, so the model can infer the pattern…
- Chain-of-thought prompting
Chain-of-thought (CoT) prompting is a technique that asks a language model to work through intermediate reasoning steps before giving a final…
- Temperature
Temperature is a setting that controls how random a language model's output is. Low values make the model pick the most likely tokens, giving…
- Reasoning model
A reasoning model is a language model trained to work through a problem in intermediate steps before giving its final answer, a process often called…
- Hallucination
In AI, a hallucination is output that sounds plausible but is false, unsupported by the model's sources or entirely made up, such as an invented…
- Context window
A context window is the maximum amount of text, measured in tokens, that a language model can take into account at one time. It includes the system…
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Further reading
- Claude Fable 5: What It Is, How to Use It, and the Prompts That Exploit It
Anthropic's first public Mythos-class model, explained with sources: pricing, safeguards, benchmarks, access methods, real use cases, and copy-paste prompts.
Frequently asked questions
Is prompt engineering still useful as models improve?
Yes, although the emphasis has shifted. Newer models follow plain instructions better, so elaborate tricks matter less. What still matters is giving clear goals, relevant context, examples of good output and a precise format, and testing the result. Those skills transfer to every model and to building AI features, agents and automations.
What is the difference between a prompt and a system prompt?
A prompt is the input you send to a model for a specific request. A system prompt is a separate set of standing instructions, usually written by the developer or set in the app, that applies to the whole conversation: the assistant's role, tone, rules and output format. The model reads both, and user prompts normally work within the limits the system prompt sets.
How long should a prompt be?
As long as it needs to be to remove ambiguity, and no longer. A one-line request is fine for simple tasks. Complex tasks benefit from a few paragraphs covering the goal, audience, source material, constraints and an example of the desired output. Very long prompts cost more tokens and can bury key instructions, so state the most important ones clearly and avoid contradictions.
Do I need a prompt engineering course or certification?
Not to use AI tools well. Free vendor documentation and practice on your own tasks cover the essentials. A course can help if you want structured practice or a credential for a job that lists it, but check what the syllabus covers, whether it includes testing and evaluation, and when it was last updated, because the field changes quickly.
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