The Best Claude Prompts (and How Claude Differs from ChatGPT)
Tested prompts plus the Claude-specific habits — XML tags, long context, careful system prompts — that get the most out of Anthropic's models.
7 min read · Updated
Quick answer
The best Claude prompts lean into what Claude does well: very long context windows, structured input using XML-style tags, and detailed system prompts. Claude responds especially well when you wrap distinct inputs in tags such as <document> and <instructions>, ask it to think inside <thinking> tags before answering, and put durable rules in the system prompt. These habits matter more on Claude than on most other models.
Anthropic’s Claude models share the same prompting fundamentals as every other model — role, task, context, constraints, example — but a few Claude-specific habits consistently improve results. This guide covers those habits and gives you tested prompts to start from.
Use XML-style tags to structure input
Claude was trained to pay close attention to XML-style tags. When a prompt mixes instructions with a long document, wrapping each part in tags removes ambiguity:
<instructions>
Extract every action item and its owner from the meeting
notes. Return a markdown table with columns: Owner, Action,
Due date. If a due date is missing, write "TBD".
</instructions>
<meeting_notes>
{notes}
</meeting_notes>This is the single most useful Claude-specific technique, and it scales: the more distinct pieces of input a prompt has, the more tags help.
Let Claude reason before answering
For analysis, maths, or multi-step decisions, ask Claude to work through its reasoning in a <thinking> block first, then give the final answer separately. This is chain-of-thought applied with structure — see our chain-of-thought guide for the underlying technique.
Think through the problem step by step inside
<thinking></thinking> tags. Then give your final
recommendation inside <answer></answer> tags.
Problem: {problem}Exploit the long context window
Claude can hold very large documents in a single prompt, which makes it strong for reviewing contracts, synthesising research, or auditing a codebase in one pass. When you do this, tell Claude where to look: “Quote the exact clause before you summarise it” keeps long-context answers grounded in the source rather than paraphrased from memory.
Put durable rules in the system prompt
Claude follows system prompts closely, so it is the right place for persona, tone, and hard constraints. Read system prompt vs user prompt for the full pattern.
A starter set of Claude prompts
- Document Q&A: “Answer only from the <document> below. Quote the relevant text, then answer. If the answer is not present, say so.”
- Refactor reviewer: “Inside <thinking>, list risks of this refactor. Inside <answer>, give a go/no-go and the top 3 tests to add.”
- Tone-matched rewrite: “Rewrite the <draft> to match the voice in <example>. Keep all facts.”
For prompts that work across every provider, start from our best work prompts and add the tag structure above.
Frequently asked questions
- How is prompting Claude different from prompting ChatGPT?
- Claude responds particularly well to XML-style tags that separate instructions from data, handles very long documents in a single prompt, and follows detailed system prompts closely. Asking Claude to reason inside explicit tags before answering also tends to improve multi-step results.
- Should I use XML tags with Claude?
- Yes. Wrapping inputs in tags such as <document>, <example>, and <instructions> helps Claude tell your data apart from your instructions, which reduces errors on long or complex prompts.
- Do prompts written for ChatGPT work on Claude?
- Usually yes, but you will get better results by adding tag-based structure and moving durable rules into the system prompt. Most prompt logic transfers; the formatting conventions are what you adapt.
Put it into practice
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