# Brand context in a system prompt

> Turn a brand context response into a system prompt: which fields to include, how to word them as instructions, and code in TypeScript and Python for Brandfetch.

Put the brand in the system prompt as instructions, not as a pasted response. A model does better with "Write plainly. Never sound like breathless hype." than with a JSON object that it must interpret. Include only the fields that the task needs. Say where the context came from.

## Which fields go in

| Task | Include | Leave out |
| --- | --- | --- |
| Copy: emails, ads, landing pages | Name, description, voice, rules, the products that the copy is about | Colors, fonts, competitors, company data |
| Visual work: images, slides, pages | Rules, colors with roles, type, the logo file to place | Long descriptions, competitors |
| Research about the company | Description, positioning, competitors, dates | Logo files |

## TypeScript

```ts
type Context = {
  meta: { canonical_name: string; resolved_at: string };
  identity: { description?: string };
  brand: { voice?: { summary?: string; attributes?: string[]; avoid?: string[] } };
};

export async function brandPrompt(domain: string): Promise<string> {
  const res = await fetch(`https://api.brandfetch.io/v2/context/${domain}`, {
    headers: {
      Authorization: `Bearer ${process.env.BRANDFETCH_API_KEY}`,
      Accept: "application/json",
    },
  });
  if (!res.ok) throw new Error(`Brand Context API answered ${res.status}`);
  const c = (await res.json()) as Context;
  const voice = c.brand.voice ?? {};
  return [
    `You are writing for ${c.meta.canonical_name}. ${c.identity.description ?? ""}`,
    "",
    "Rules. Follow these exactly:",
    ...(voice.avoid ?? []).map((tone) => `- Never sound like ${tone}.`),
    "",
    "Voice:",
    voice.summary ?? "",
    `Sounds: ${(voice.attributes ?? []).join(", ")}`,
    "",
    `Source: read from ${c.meta.canonical_name}'s public website on ${c.meta.resolved_at.slice(0, 10)}.`,
    "Don't state prices, numbers or claims from it as fact without a check.",
  ].join("\n");
}
```

## Python

```python
import os
import httpx

def brand_prompt(domain: str) -> str:
    res = httpx.get(
        f"https://api.brandfetch.io/v2/context/{domain}",
        headers={
            "Authorization": f"Bearer {os.environ['BRANDFETCH_API_KEY']}",
            "Accept": "application/json",
        },
        timeout=30,
    )
    res.raise_for_status()
    c = res.json()
    voice = c["brand"].get("voice") or {}
    lines = [
        f"You are writing for {c['meta']['canonical_name']}. {c['identity'].get('description', '')}",
        "",
        "Rules. Follow these exactly:",
        *[f"- Never sound like {tone}." for tone in voice.get("avoid", [])],
        "",
        "Voice:",
        voice.get("summary", ""),
        f"Sounds: {', '.join(voice.get('attributes', []))}",
        "",
        f"Source: read from the public website on {c['meta']['resolved_at'][:10]}.",
        "Don't state prices, numbers or claims from it as fact without a check.",
    ]
    return "\n".join(lines)
```

For linear.app, the rules section contains "Never sound like breathless hype." and "Never sound like folksy banter." These two lines are everything that the response tells a model not to do.

## With a maintained Brief

Maintained context is context prepared from sources added for the brand. The `guidance` in a Rainbrand Brief is Markdown for one task. Put it in the prompt without changes, below one line that says what the task is. Add the version from `skillId` to the source line. The [system prompt template](https://brandcontextapi.com/templates/system-prompt) shows both versions filled in. [JSON or Markdown?](https://brandcontextapi.com/recipes/json-or-markdown) tells you when to keep the structure.

Before you pass Brandfetch content to a model, read the Brandfetch terms. The [Brand Context API page](https://brandcontextapi.com/responses/brandfetch-brand-context-api#before-you-pass-it-to-a-model) gives the details.

---

Source: https://brandcontextapi.com/recipes/system-prompt
Last updated: 2026-11-11
Published by The Brand Object. The Brand Object is run by Rainbrand, which makes two of the endpoints described here. Their pages follow the same rules as the others.