Prompt Engineering

Prompt Variables Extractor

Extract, inspect, and test prompt template placeholders across all major syntax conventions ({{mustache}}, {f-string}, [BRACKETS], <angles>, $VARS). Test live variable resolution with 1-click mock data and generate ready-to-use TypeScript interfaces, Python Pydantic models, JSON Schemas, and LangChain templates.

Unique Variables
10placeholders
Total Injections
10occurrences
Syntax Formats
5({{}}, {}, [], $, {})
Live Fill Status
0/10fields populated
Samples:
Prompt Template
Variable Inputs (10)
agent_rolemustache
language_codemustache
cta_urlmustache
sender_titlemustache
product_categoryfstring
COMPANY_NAMEbracket
TARGET_AUDIENCEbracket
PRICING_TIERdollar
SENDER_NAMEdollar
product_updateangle
Resolved Prompt

Example Walkthrough

Realistic transform preview

Input (Prompt template)

You are an AI assistant acting as {{agent_role}} for [COMPANY_NAME].

Context & Background:
Target Audience: [TARGET_AUDIENCE]
Product Category: {product_category}
Language: {{language_code}}

Task:
Draft a high-converting email newsletter about <product_update>. Highlight key features, pricing ($PRICING_TIER), and include a call-to-action button linking to {{cta_url}}.

Sign off respectfully as $SENDER_NAME, {{sender_title}}.

Output Result

agent_role
language_code
cta_url
sender_title
product_category
COMPANY_NAME
TARGET_AUDIENCE
PRICING_TIER
SENDER_NAME
product_update

About this tool

Production AI agents, RAG pipelines, and automated customer workflows rely heavily on prompt templates: parameterized text blueprints dynamically injected with user attributes, database records, and runtime context. As prompt architectures grow in complexity, untracked variables, conflicting syntax conventions (e.g. mixing `{var}` with `[VAR]` and `{{var}}`), and misspelled keys lead to catastrophic runtime injection bugs.

Our Prompt Variables Extractor & Template Studio is a comprehensive developer suite for scanning, testing, and standardizing prompt placeholders. The multi-syntax parser identifies all standard conventions: Mustache/Handlebars/Jinja2 (`{{var}}`), Python f-strings / LangChain (`{var}`), Square Bracket Capitals (`[VAR]`), Angle Brackets (`<var>`), and Shell/Dollar syntax (`$VAR` / `${var}`).

The interactive workbench includes a dynamic Live Testing Playground: an automated form that allows engineers to populate sample values or click "Mock Fill" to preview the fully interpolated prompt in real time. This guarantees that your prompt reads naturally and formats correctly before committing code to production.

To accelerate engineering integration, the tool features 1-click Code Schema Generation: generating type-safe TypeScript Interfaces, Python Pydantic Models (`BaseModel`), JSON Schemas, and LangChain Python/TS `PromptTemplate` instantiation snippets directly from your template placeholders.

How it works

  1. 1 1. Paste Prompt Template: Paste your parameterized prompt blueprint into the template editor.
  2. 2 2. Automatic Multi-Syntax Extraction: The engine instantly parses and deduplicates all variable placeholders across {{}}, {}, [], <>, and $ styles.
  3. 3 3. Test Live Variable Interpolation: Switch to the Live Tester tab to type in custom values or click "Mock Fill" to preview the resolved prompt.
  4. 4 4. Generate Type-Safe Schemas: Switch to Code Schemas to export TypeScript Interfaces, Python Pydantic models, JSON Schema, or LangChain snippets.
  5. 5 5. Normalize Template Syntax: Use the Syntax Converter to standardize mixed templates into uniform {{mustache}} or {python} formats with 1 click.
  6. 6 6. Copy or Download: Export variable lists (Comma-separated, JSON Array, Python List), resolved text, or code files directly into your codebase.

Benefits

  • Prevents Missing Key Bugs: Identifies every required runtime placeholder so API requests never fail due to undefined parameters.
  • Interactive Live Playground: Test how the prompt reads when variables are populated with real-world or mock data before deployment.
  • 1-Click Mock Data Generator: Automatically generates context-aware sample values (e.g. roles, company names, URLs, prices) with a single click.
  • Developer Code Generation: Exports ready-to-use TypeScript interfaces, Python Pydantic classes, JSON Schemas, and LangChain code.
  • Multi-Syntax Normalization: Converts inconsistent templates containing mixed brackets or shell variables into a unified syntax standard.
  • Variable Frequency & Analytics: Tracks total occurrences and placeholder distribution to help eliminate redundant template logic.
  • 100% Client-Side Privacy: No confidential prompt templates, API parameters, or customer schemas are transmitted to external servers.

Use cases

  • LangChain & LlamaIndex App Development: Extract `input_variables` lists and generate Python/TypeScript prompt classes.
  • API Parameter Verification: Create JSON Schema validation rules for backend endpoints that dynamically format LLM system prompts.
  • Prompt Template Refactoring: Normalize legacy templates using uppercase bracket notation (`[USER_NAME]`) into standard Jinja2 `{{user_name}}`.
  • Quality Assurance & Evals: Test prompt templates against edge-case input values (e.g. long strings, special characters) in the live playground.
  • Documentation & Team Handoffs: Generate clean checklists and parameter dictionaries for non-technical stakeholders configuring prompts.

Tips

  • Standardize on double curly braces (`{{variable_name}}`) for Jinja2/Handlebars templates to avoid conflicts with single curly braces in JSON snippets.
  • Use descriptive snake_case variable names (`{{customer_account_id}}` rather than `{{id}}`) to generate self-documenting Pydantic models.
  • Run your template through the Live Tester with extra-long mock strings to verify that formatting constraints and line wraps hold up.
  • Pair this tool with the Prompt Formatter to structure your template into Claude XML or Markdown sections after extracting variables.

Frequently asked questions

What is a prompt variables extractor?

A prompt variables extractor is a developer utility that scans prompt templates to identify all parameterized placeholders (such as {{user_name}}, {variable}, or [COMPANY]) and generates type-safe code schemas and live test forms.

What is the best prompt template variables tester?

DevForge Prompt Variables Extractor is the best template tester because it supports all 5 major placeholder syntaxes ({{mustache}}, {python}, [BRACKET], <angle>, $VAR), features a live testing playground with 1-click mock data, and exports TypeScript, Python Pydantic, JSON Schema, and LangChain templates.

Which prompt placeholder formats are supported?

The extractor supports Mustache/Handlebars/Jinja2 ({{var}}), Python f-strings ({var}), Square Bracket Capitals ([VARIABLE]), Shell/Dollar syntax ($VAR or ${var}), and Angle Brackets (<var>).

How does the Live Variable Tester work?

The Live Tester generates interactive input fields for every unique placeholder detected in your template. As you type into the inputs (or click "Mock Fill"), the preview pane updates instantly with the fully resolved, interpolated prompt.

What code schemas can this tool generate?

It generates TypeScript Interfaces, Python Pydantic BaseModel classes, standard JSON Schema definitions, and LangChain Python/TypeScript PromptTemplate initialization code.

Can I convert my template from one syntax to another?

Yes. The Syntax Converter tab lets you convert mixed or legacy templates (e.g. [USER_NAME] or {topic}) into your chosen standard (such as {{user_name}} or ${user_name}) with one click.

How does it handle duplicate variables in a prompt?

The tool extracts all instances, deduplicates the variable list for schema generation, and displays an occurrence badge (e.g. 3x) showing how often each variable appears.

Is my template and variable data secure?

Yes. All extraction, schema generation, and live interpolation execute 100% locally in your browser memory via JavaScript. No prompt text or variable values are ever uploaded.

Related tools

Prompt Formatter Structure AI prompts with Claude XML, OpenAI Markdown, Few-Shot blocks, and API JSON. Prompt Cleaner Sanitize smart quotes, chat log prefixes, invisible Unicode, and copy-paste junk from prompts. Prompt Token Counter Count AI tokens, visualize subword slices, and estimate API costs across GPT-4o, Claude & Gemini. JSON Extractor Extract, inspect, tree-view, and semantically diff JSON from messy text, logs, and AI outputs.

Related articles