Prompt Builder - Free Online Tool | PivaBox

Build multi-turn prompts with role assignment

AI Prompt Builder — Construct Multi-Turn Conversation Payloads with Role Assignment, Variable Templating, and API-Ready JSON Export

1. Build your conversation structure by adding message blocks and assigning each one a role: system for setting the AI's behavior and constraints (the system prompt), user for human queries and instructions, and assistant for pre-populating example responses or establishing conversation tone through few-shot examples. The multi-message layout mirrors the exact structure used by the OpenAI Chat Completions API and Anthropic Messages API, giving you full control over conversation flow, context injection, and response priming — all without writing raw JSON by hand.
2. Define reusable variables in the Variables panel using key=value format — one per line. In your messages, use ALL_CAPS_PLACEHOLDERS like NAME, TOPIC, or USER_QUERY. When you copy the output, the tool automatically interpolates your variable values into the message content. This template system lets you create a single prompt structure once and reuse it across different inputs — perfect for building customer support bots, content generators, or educational AI assistants that follow consistent patterns but handle varying user data.
3. Review the real-time JSON preview in the right panel — it shows exactly what your API payload will look like, including the model and max_tokens fields pre-populated for convenience. Click Copy to grab the complete JSON payload to your clipboard, ready to paste into your API client, cURL command, Python script, or directly into the OpenAI Playground. The output follows the standard chat completion format compatible with OpenAI GPT models, Anthropic Claude, and most other LLM providers that support the messages array convention.

Frequently Asked Questions

How does Prompt Builder compare to writing prompts directly in a text editor or API playground?

PivaBox Prompt Builder offers three key advantages over raw editing. First, the structured role assignment ensures you never accidentally format messages incorrectly — a common source of API errors when hand-writing JSON. Second, the variable template system separates prompt logic from input data, making your prompts reusable and maintainable across different use cases (swap in different customer names, topics, or contexts without rewriting the entire prompt each time). Third, the real-time JSON preview shows you exactly what the API will receive, catching formatting issues before they become runtime errors. Like all PivaBox, Prompt Builder runs entirely in your browser — your prompt designs and variable values never touch a server, which is critical when working with proprietary prompt engineering IP or sensitive business logic.

What API providers and models is the generated JSON compatible with?

The generated JSON follows the industry-standard chat completion message format used by OpenAI (GPT-4, GPT-4o, GPT-3.5), Anthropic (Claude Opus, Sonnet, Haiku), Google (Gemini via compatible endpoint), Mistral, and most open-source model serving platforms (Ollama, vLLM, LocalAI). The output includes a <code>model</code> field pre-set to <code>claude-sonnet-4</code> and <code>max_tokens</code> set to <code>4096</code> — you can easily change these values before sending to match your target model and desired response length. The messages array structure (with <code>role</code> and <code>content</code> fields) is the de facto standard for LLM chat APIs.

Can I save and reuse my prompt templates, and what are best practices for template design?

The Prompt Builder does not persist data between sessions by design — this ensures your prompt engineering work remains private and never stored on any server. To save your templates, we recommend copying the output JSON and saving it to a <code>.json</code> file in your project repository, or keeping a text file with your message structures and variable definitions for quick copy-paste. Best practices for effective prompt templates: (1) Keep system prompts focused and specific — define the AI's role, tone, constraints, and output format clearly. (2) Use variables for dynamic elements that change per request (user names, topics, dates) while keeping the prompt structure static. (3) Include example <strong>assistant</strong> messages to demonstrate desired response style through few-shot learning. (4) Test your template with diverse variable values to ensure consistent behavior across edge cases.