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Prompt builder

Fill in what you know: the task, the context, the format you want back. The builder assembles a clear, structured prompt that works across models. Copy it, download it or count its tokens.

Works with any chat modelNothing you type leaves your browser

Your prompt

Want ready-made prompts? Browse the prompt library.

The anatomy of a strong prompt

Most weak prompts fail for the same reason: the writer knows things the model doesn't. The builder walks you through the parts that close that gap.

  • Task. One clear instruction with a verb: summarize, extract, rewrite, review, decide.
  • Context. Who it's for, why it matters, what you'll do with the answer and what a great result looks like.
  • Input. The material to work on, fenced off with tags so it can't be confused with instructions.
  • Format. The exact shape you want back. For anything a program will read, ask for JSON with named fields.
  • Rules and an example. Constraints the model can check itself against, and one good answer to imitate.

Then test it: run the prompt on five real inputs, note where it goes wrong, and add a rule or example for each failure. Our prompt engineering guide covers techniques, reasoning models and versioning.

Need hands-on help building it?

RAG, agents, fine-tuning or a production chatbot: describe the project and we'll suggest an approach and, if you want, partners who build it.

  • Free and vendor-neutral: no model provider pays for placement.
  • Your details go only to partners you agree to.

Takes about 2 minutes. No sales calls unless you ask for one.

Frequently asked questions

Why use tags like <task> and <context> in a prompt?

Clear delimiters separate your instructions from the material the model should work on. That reduces confusion, makes long prompts easier to maintain, and lowers the risk that text inside a document is mistaken for an instruction. Most current models handle XML-style tags well.

Should I tell the model to think step by step?

For standard chat models, asking for step-by-step reasoning can improve answers to multi-step problems. Reasoning models already think before they answer, so providers generally advise against prescribing the steps; ask for the final answer and its format instead.

What makes a prompt good?

A specific task, the context a smart newcomer would need, an example or two of a good answer, and an explicit format. Most weak prompts fail because they leave out context the writer took for granted.