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Anthropic Prompt Engineering
Learn when prompt engineering is the right solution, and find Claude prompting techniques and interactive tutorials.
About Anthropic Prompt Engineering
This is the prompt engineering overview section of the Claude Platform Docs. It explains when prompt engineering is the appropriate fix for a problem, points to prompting best practices as the main reference for techniques such as clarity, examples, XML structuring, role prompting, thinking and prompt chaining, and links to model-specific guidance for Claude models. It also covers related topics including defining success and building evaluations, reducing latency, reducing hallucinations, increasing output consistency, mitigating jailbreaks and reducing prompt leaks.
The guide assumes the reader already has clear success criteria, a way to test against them empirically, and a first draft prompt. It states that not every failing evaluation is best solved by prompt engineering, giving model selection as an example where latency and cost may be improved more easily. Learning options include an example-filled GitHub tutorial, a lighter interactive Google Sheets spreadsheet version, and a metaprompt notebook for generating a first draft. The site presents a cookie banner with options to customize, reject or accept cookies.
Key features
- Explains when prompt engineering is the right solution for a problem
- Model-specific prompting guidance for current Claude models
- Covers clarity, examples, XML structuring, role prompting and prompt chaining
- Interactive GitHub tutorial and lighter Google Sheets spreadsheet tutorial
- Metaprompt recipe notebook for generating a first draft prompt
- Guidance on hallucinations, output consistency, jailbreaks and prompt leaks
Best for: Developers and writers working with Claude models who already have a draft prompt, defined success criteria and a way to test results.
Good for
- States when prompt engineering is not the right fix, for example where model choice may improve latency or cost
- Described as a living reference that is updated alongside model-specific guidance
- Offers written reference material plus interactive tutorial alternatives for different learning preferences
Watch out for
- Assumes a defined success criteria, empirical testing method and first draft prompt before starting
- The GitHub tutorial was written for Claude 3 models, so it may differ from current best practices
- Documentation is provided in English only
Anthropic Prompt Engineering at a glance
- Category
- AI & Machine Learning › AI for Education
- Type
- Online tool
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Anthropic Prompt Engineering FAQ
What should I have before starting this guide?
A clear definition of the success criteria for your use case, some ways to empirically test against those criteria, and a first draft prompt you want to improve.
Is prompt engineering always the right solution?
No. The guide notes that not every success criteria or failing evaluation is best solved by prompt engineering, and that latency and cost can sometimes be improved more easily by selecting a different model.
Are there interactive ways to learn the material?
Yes. Options include an example-filled GitHub tutorial, a lighter interactive Google Sheets spreadsheet tutorial, and a metaprompt recipe notebook from the Claude Cookbook for generating a first draft prompt.
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