Prompting Claude

Claude is the AI assistant made by Anthropic. It is available as a chat app, inside developer tools and through an API, and the same prompting habits carry across all of them.

Anthropic's documentation offers a useful mental model: write for a very capable new colleague who has no background on your project. That person would need to know what the work is for, who will read it, what a good result looks like and what to avoid. A prompt that answers those questions usually beats a short command, and it beats a long prompt stuffed with emphasis and repeated warnings.

There are two places to put guidance. A system prompt, or in the app the instructions attached to a project and your saved preferences, sets a role and standing rules. The conversation itself carries the task. Reference material can be pasted, uploaded, or stored as project knowledge so it is available in every chat within that project.

Claude handles long inputs well when they are organized. Anthropic recommends wrapping distinct parts of a prompt in XML-style tags, for example one tag around a contract and another around your instructions, so each part is unambiguous. The tag names are yours to choose; what matters is that they are consistent and descriptive. The labeled sections used in the prompts on this site serve the same purpose and can be converted to tags if you prefer.

For complicated work, one large prompt is not always the right tool. Splitting a job into a sequence, where the output of one step becomes the input of the next, makes each step easier to check and correct.

Specific model names, limits and plan features change frequently and are left out here on purpose. Anthropic's documentation is the place to confirm current details.

How to get better results

Be clear and direct, and include the reason

Anthropic's first recommendation is to be explicit about what you want and to supply context: the purpose of the task, the audience, and where the output will be used. Explaining why a constraint exists tends to work better than stating the constraint alone, because the model can apply the intent to cases you did not list. If you want thorough, detailed output, ask for it specifically.

Use examples to pin down format and tone

The documentation describes multishot prompting, giving a few worked examples, as one of the most dependable ways to get consistent structure and style. Make the examples relevant to your real case and different from one another, so the pattern is learned and not a single instance. Placing them inside a clearly marked block keeps them from being confused with the instructions around them.

Mark up the parts of a prompt with XML-style tags

When a prompt mixes instructions, documents, data and examples, Anthropic suggests wrapping each in its own tag. This reduces the chance that pasted content is treated as a command and makes it easy to refer to a part by name later in the prompt. You can also ask for the answer inside named tags, which helps when you need to pull one section out of a longer reply.

Set a role in the system prompt or project instructions

Assigning a role, such as a contracts reviewer or a senior data analyst, focuses vocabulary, depth and priorities. Anthropic's guidance is to put the role and standing rules in the system prompt and keep task detail in the user turn. In the app, project instructions do this job. Keep the role specific to the work; a vague persona adds little.

For long documents, put the material first and the question last

Anthropic's long-context advice is to place lengthy documents near the top of the prompt and your instructions or question after them. When there are several documents, label each with its source so the answer can say which one a point came from. Asking for the relevant quotations to be extracted first, before the analysis, helps keep the response tied to the text.

Break complex jobs into a chain of prompts

The documentation recommends chaining for multi-stage work: research, then outline, then draft, then review, each as its own prompt with a single clear objective. Smaller steps are easier to inspect, and a weak stage can be rerun without redoing the rest. Asking Claude to review its own earlier output against your criteria is a legitimate link in that chain.

Where it fits

Well suited to

  • Working with long documents or several documents at once, when they are clearly labeled
  • Writing and editing where voice, audience and constraints are spelled out
  • Analysis that benefits from reasoning through steps before giving a conclusion
  • Coding tasks such as review, refactoring and explanation with the relevant files supplied
  • Ongoing work organized in a project with shared instructions and reference files

Watch out for

  • It can still produce inaccurate statements; check facts, citations and calculations that matter
  • Instructions are taken fairly literally, so a request for 'suggestions' may yield suggestions and not the finished change
  • Very long conversations can dilute early instructions; restate constraints or begin a new chat
  • App features and usage limits differ by plan and change over time
  • Follow your organization's rules on sharing confidential or personal information

Prompts that work with Claude

These prompts are expected to work with Claude based on their structure. Check each prompt page for its testing status; see how compatibility is labelled.

Web Design

Web Development

Content Writing

SEO

Marketing

Business

Research

Coding

Graphic Design

Productivity

Education

E-commerce

Official documentation