How to write brand guidelines AI assistants can use (ChatGPT, Gemini, Claude)

Your team — and increasingly, AI assistants — need to know your brand voice, colors and rules. Write your brand guidelines in a format both humans and LLMs can follow.

7 min read intermediateUpdated Jun 23, 2026
BI
Reviewed by the editorial team · Jun 23, 2026

AI assistants now write a huge share of customer-facing copy, social posts and even ad creative. If your brand guidelines aren't machine-readable, every output will be off-brand by default.

The fix: publish guidelines as clean, structured Markdown or HTML on your site, with explicit rules an LLM can apply.

Before you start
  • An existing brand identity — logo, colors, fonts and a sense of voice

Step by step

  1. 01

    Structure the doc with H2 sections

    Voice, Tone, Vocabulary, Colors, Typography, Logo Usage, Imagery, Examples. AI parses sections; one wall of text gets ignored.

  2. 02

    Write voice rules as explicit do/don'ts

    'Do: write in second person. Don't: use corporate jargon like "synergy" or "leverage".' Explicit beats abstract every time.

  3. 03

    List approved and banned vocabulary

    Two columns. 'Use: customers, members, you. Avoid: users, consumers, leveraging.' This is the single most useful section for AI to follow.

  4. 04

    Publish exact color values

    Hex, RGB and HSL for every color, with the role ('Primary CTA', 'Surface', 'Text on dark'). Avoid name-only references like 'our blue'.

  5. 05

    Add example outputs

    Three example tweets, three example email subject lines, three example product descriptions — labelled 'on-brand' and 'off-brand'. LLMs learn from examples faster than rules.

  6. 06

    Publish as a public web page

    Even a hidden URL works — as long as ChatGPT can fetch it. Add the URL to your llms.txt file so AI assistants find it automatically.

Key takeaways

  • Brand guidelines are no longer just for designers — they're prompts for every AI tool your team uses.
  • Explicit lists of approved vocabulary outperform abstract voice descriptions.
  • Examples are more durable than rules — both for humans and LLMs.
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