Prompt Template Generator
Make a prompt that worked once reusable. Paste the concrete prompt and list the parts that change – “Maya => first_name”, “14 March => trial_end” – or leave the list empty and let the tool suggest variables for dates, numbers, emails, URLs and quoted text. Choose the variable syntax your tools use and get the template, the template plus its variables as JSON, or ready-to-run Python code for LangChain’s PromptTemplate.
- Runs in your browser
- No sign-up
- Free to use
How to use Prompt Template Generator
- Paste a concrete prompt.
- List text => variable_name pairs, or let it suggest.
- Choose the syntax and output.
- Copy the template.
Prompt Template Generator features
Five syntaxes
Mustache, Jinja, f-string, ${}, <name>.
Auto-suggest
Dates, numbers, emails, URLs, quotes.
LangChain code
PromptTemplate example.
Brace escaping
Literal braces doubled for f-strings.
Copy or download
One click to the clipboard or a .txt file.
Private
Built in your browser; nothing is sent to an AI service.
When to use Prompt Template Generator
- Building prompt libraries.
- Moving prompts into code.
- Sharing prompts with a team.
- Preparing batch jobs.
Prompt Template Generator FAQ
Which syntax should I use?
The one your tool expects: {{name}} for Mustache-based tools, {{ name }} for Jinja, {name} for Python and LangChain.
Why are some braces doubled?
In f-strings and LangChain templates, literal { and } must be written as {{ and }}.
Longer phrases first?
Yes – replacements are applied longest first so “14 March” is not broken by “14”.
How do I fill the template?
With the Prompt Variable Generator, including CSV batches.
From one prompt to many
Prompts are easiest to improve when the instructions are separated from the data. A template keeps the instructions in one place and the values change per use.
Name variables after what they mean (customer_name, deadline) rather than their value.
This tool does not call an AI model. It turns your answers into a well-structured prompt in your browser, so it costs nothing, works offline once loaded and never sends your text anywhere. Paste the result into ChatGPT, Claude, Gemini, Copilot or any other assistant, or into the system message of an API call.
Good prompts are specific about the task, the context the model cannot know, the audience and the shape of the answer. The fields on this page cover exactly those parts; leave optional fields empty when they do not apply, and the prompt stays short.
Review the answer you get: language models can be confidently wrong, especially about facts, numbers, names and recent events. Ask for sources or mark claims to check, and keep personal or confidential data out of prompts unless your organisation allows it.
Keep the prompts that work. A small library of tested prompts – with a note on which model and settings they were used with – saves time and makes results more consistent across a team. The other prompt tools on this site help you format, template, fill and measure them.