Prompt Template Tester
Render LLM prompts with variables across Simple, Handlebars, Jinja, and LangChain syntaxes
Frequently Asked Questions
What is the Prompt Template Tester?
A prompt is rarely one fixed string — it has variables like `{{user_name}}`, `{{context}}`, or `{{examples}}` that change per request. This tool renders your template with one or more variable sets side by side, so you can see what the model will actually receive before hitting the API.
Which template syntaxes are supported?
- **Simple**: `{{variable}}` (no logic) - **Handlebars**: `{{#if}}`, `{{#each}}`, helpers — widely used in TypeScript LLM apps - **Jinja**: `{{ var }}`, `{% for %}`, `{% if %}` — the default for Python / LangChain - **LangChain f-string**: `{variable}` — matches the `PromptTemplate` default Pick whichever matches the backend you ship to.
When do I need multiple variable sets?
Two common flows: (1) A/B testing the same template against different user inputs — e.g. a polite vs. rude user message — to see how wording shifts the prompt; (2) few-shot prompting where you iterate on which examples to include. Side-by-side rendering surfaces whitespace and escaping issues that hand-building misses.
Does it estimate tokens?
Yes. Each rendered variant shows an approximate input token count using the `cl100k` tokenizer (the closest portable proxy across GPT / Claude / Llama). For precise per-model counts use the LLM Token Counter — they are linked as related tools.
What's the difference between Jinja and Handlebars here?
For straight interpolation they behave identically. Differences show up in control flow: Jinja uses `{% if x %}...{% endif %}` and dotted access `user.name`; Handlebars uses `{{#if x}}...{{/if}}` and helpers like `{{#each items}}`. The parser reports template errors inline so you can fix mismatched braces without running the API.
Is my prompt uploaded anywhere?
No. Rendering happens entirely in the browser — safe for proprietary prompts, internal system messages, and customer PII used in few-shot examples.