Count tokens for GPT-4, Claude, Gemini and more. Paste any text or entire documents to see exact token usage before sending to an AI model.
Convert large JSON datasets into clean, token-efficient AI prompts. Perfect for feeding structured data to ChatGPT, Claude, or Gemini without wasting context.
Transform CSV files and large tabular datasets into AI-ready prompts. Control which columns to include, row limits, and output format to stay within token budgets.
Generate professional system prompts for AI assistants, chatbots, and agents. Choose persona, tone, domain, and constraints to produce a production-ready system prompt instantly.
Calculate whether your text fits within any AI model's context window. Compare token usage across GPT-4o, Claude 3.5, Gemini 1.5 Pro, Llama 3, and more.
Compare GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3, Mistral, and more side-by-side. Context window, pricing, strengths, speed, and best use cases.
Scheduling meetings across time zones? Double-check the time with a World Clock to avoid showing up three hours early (or late!).
Vague prompts get vague results. The AI Prompt Builder guides you through the six components of a well-structured prompt — Role, Context, Task, Input Data, Output Format, and Constraints — and assembles them into a single, ready-to-paste prompt for any LLM. Whether you're new to prompt engineering or just want a consistent structure for complex requests, the builder prevents the most common mistakes (no context, no format instructions, no constraints) and outputs a prompt you can copy directly into ChatGPT, Claude, or Gemini.
The builder concatenates each enabled section's content with clear section labels, producing a structured prompt that gives the AI model all the context it needs. Sections are optional — enable only what's relevant for your use case. The output is plain text formatted for direct use in any LLM interface or API call.
Prompt engineering is the practice of designing inputs to AI models to reliably get the desired output. Research consistently shows that prompts with explicit role definitions, context, and constraints outperform unstructured requests. The "Role-Context-Task-Format-Constraints" framework is one of the most widely used structures because it maps directly to how LLMs were fine-tuned to follow instructions.
No — toggle on only the sections relevant to your task. For simple requests, just a Task section may be enough. For complex workflows, adding Role, Context, and Constraints significantly improves output quality.
Currently the tool doesn't persist prompts between sessions. We recommend copying your assembled prompt and saving it in a notes app or document for reuse.
The builder is designed for text-based LLMs. For image generation (Midjourney, DALL-E, Stable Diffusion), prompt structure is quite different and the builder's format may not apply directly.
All calculations and data processing for this tool are performed locally in your browser. UtilToolkits does not send any of your data to an external server, ensuring your information remains private and secure.
Structure your prompts with proven engineering principles. Toggle sections on/off, fill in what you need, and copy a production-ready prompt in seconds.
Role first, always
Starting with a specific expert persona consistently improves output quality — even for simple tasks.
Examples beat instructions
Showing 2–3 input/output examples is more reliable than describing the format in words.
Watch your token budget
Each section adds tokens. Use the token counter to ensure your full prompt + data fits the model's context window.
Constraints matter
Telling the AI what NOT to do (no jargon, no bullet points, no markdown) prevents common output issues.