Configure your style guide
Fill in the options on the left and click Generate// define tone, format & rules for consistent AI output
Create reusable formatting and tone guidelines for AI-generated content. Define writing style, tone, format rules, and output structure for consistent AI responses.
Configure your style guide
Fill in the options on the left and click GenerateGive it a descriptive name like "Support Bot Style" or "Blog AI Rules".
Select tone, audience, format, and response length from the dropdowns.
Paste any specific things to avoid or extra formatting rules — one per line.
Click Generate, then copy as Markdown, system prompt, or download the file.
This tool helps you create a structured, reusable style guide for AI-generated content. Whether you're configuring a system prompt for ChatGPT, Claude, Gemini, or a custom LLM — consistent tone and format rules dramatically improve output quality.
Export as Markdown to document your guidelines, or copy the system-prompt-ready version to paste directly into your AI tool of choice.
An AI output style guide is a set of rules that defines how an AI model should write — covering tone, formatting, response length, things to avoid, and audience expectations. It helps ensure consistent, predictable output across different prompts and sessions.
Copy the Markdown version to a shared doc for your team, or use the "Copy as System Prompt" button to get a condensed version you can paste directly into a system prompt field in ChatGPT, Claude, Gemini, or any custom LLM interface.
No. The style guide is generated server-side from your selected options and returned to your browser. No data is stored, logged, or shared with third parties.
Yes — use the "Custom Rules" and "Things to Avoid" text areas to add any additional guidance, one rule per line. These are included verbatim in the generated style guide.
You can copy the guide as raw Markdown text, copy it as a condensed system prompt snippet, or download the full guide as a .md file or a structured .json file for programmatic use.
The generated style guides are plain text and work with any AI platform that accepts system prompts or custom instructions — including ChatGPT, Claude, Gemini, Mistral, Llama-based tools, and any custom API-powered chat application.
An AI output style guide is a structured document — or system prompt — that defines how an AI assistant should communicate. It covers tone of voice, formatting preferences, response length, audience assumptions, and specific rules about what to include or avoid. Just as human writers follow editorial style guides (AP Style, Chicago Manual), AI models benefit enormously from explicit, consistent formatting and tone instructions.
Without a style guide, the same AI model can produce wildly different outputs depending on how a question is phrased, which session it's in, or even which day the request is made. A well-crafted style guide acts as a stable anchor, ensuring every response feels coherent, on-brand, and professionally formatted.
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Inconsistency is one of the biggest practical challenges when deploying AI in production. A customer support bot that switches between friendly and formal tones mid-conversation erodes trust. A blog writing assistant that randomly uses bullet points in one post and dense paragraphs in another creates editing headaches. A code-generation assistant that sometimes adds comments and sometimes doesn't wastes developer time.
By defining your output rules upfront and encoding them into a reusable style guide, you solve these problems at the source rather than patch-fixing them after the fact. Style guides are particularly valuable for:
A useful AI style guide typically covers five key areas:
1. Tone and Voice. This is the most impactful single variable. "Professional and authoritative" produces very different output from "warm and casual" — even for identical questions. Be specific: instead of just "professional," specify whether contractions are acceptable, whether humour is appropriate, and whether the AI should use first-person ("I recommend…") or impersonal constructions ("It is recommended that…").
2. Target Audience. Defining who the reader is allows the AI to calibrate vocabulary, assumed knowledge, and explanation depth. A guide targeting senior engineers can skip basic explanations; one targeting first-time users should not assume any prior knowledge.
3. Output Format. Should responses use bullet points or flowing prose? Should headers be included? Are markdown code blocks appropriate? Format rules prevent the AI from defaulting to its own preferences, which vary significantly between models.
4. Response Length. "Concise" and "detailed" mean different things to different models. Explicitly specifying expected length (e.g., "150–400 words" or "maximum 5 bullet points") removes ambiguity and prevents both over-explanation and unhelpfully terse replies.
5. Explicit Avoidance Rules. These are often the most valuable. Common entries include: avoid filler openers like "Certainly!" or "Great question!"; avoid excessive disclaimers; do not repeat the user's question back before answering; avoid hedging with "it depends" without following up with actual guidance.
Style guides exist in two forms: as documentation (shared with humans) and as system prompts (injected directly into an AI session). This tool generates both. The Markdown version is intended for human readers — team wikis, onboarding docs, editorial guidelines. The "Copy as System Prompt" export is stripped down and optimised for direct injection into the system prompt field of ChatGPT Custom Instructions, Claude Projects, or any API-powered AI workflow.
For best results with system prompts, keep the rules directive rather than descriptive. "Use bullet points for lists of more than 3 items" works better than "I prefer bullet points." Direct instructions reduce interpretation variance across different LLMs.
The JSON export from this tool is designed for programmatic reuse. You can parse the file in your backend to dynamically inject the appropriate style guide based on context — for example, using a "technical" style for developer-facing tools and a "friendly" style for end-user support, from a single codebase. Version control your style guide files alongside your prompts in your repository for full traceability.
For teams, the Markdown download makes an excellent starting point for a "Prompt Standards" section in internal documentation. Regularly reviewing and updating your style guide as you observe AI outputs in production is a best practice — the guide should evolve alongside your understanding of how your chosen model interprets instructions.