Custom GPT for Content Creation: Build a Reusable Workflow

A custom GPT for content creation gives the assistant your voice rules, your source standards, and your review steps so every piece starts from the same baseline. Without those inputs, an AI assistant defaults to a neutral tone and general knowledge, which may not match what your brand or audience expects.

The editorial decisions behind a good content GPT are worth keeping outside the tool itself. fenbs stores your approved prompts and decisions so Claude, Cursor, or any other connected assistant can use the same rules when you need a second tool for a different task.

Start with a voice brief in the instructions

A voice brief tells the assistant how your content sounds. It is different from a general style guide: it focuses only on the choices the assistant needs to make in every piece. A general style guide covers formatting, grammar, and brand standards for a whole organization. A voice brief for a custom GPT covers only the handful of decisions that directly affect the tone and shape of every sentence it writes.

A working voice brief covers:

• Tone: "Direct and plain, no hype or exclamation marks. Write as if explaining to a smart colleague, not pitching to a buyer."
• Person and address: "Second person. Address the reader as you. Do not use we except when quoting the organization."
• Sentence length: "Short sentences. One idea per sentence. No lists of adjectives."
• Words to avoid: list any branded terms, competitor names, or phrases your audience dislikes.
• A worked example: show a paragraph written correctly, and if useful, the same paragraph rewritten badly.

Add example inputs and outputs

After the voice brief, add two or three examples of real inputs and the correct outputs for those inputs. Examples do more work than rules. A sentence saying "keep intros under fifty words" competes with many other instructions; an example that shows a fifty-word intro and a longer one marked as too long is harder to ignore.

Use real content from your work if you can, with permission. A fictional example that does not sound like your actual content will produce fictional-sounding output.

Set source requirements

If your content must cite sources, be explicit about what counts as a source and what does not. A GPT with no source rules will use whatever information it has, which may include inaccurate or outdated material. Source requirements to consider:

• Which source types are acceptable: primary research, named companies' own documentation, government data.
• Which are not: third-party summaries, roundup articles, anonymous posts.
• What to do when a claim cannot be sourced from an acceptable source: "If you cannot cite an acceptable source for a claim, flag it with [needs source] instead of stating it as fact."

Add a review checklist

A custom GPT cannot review its own output for accuracy. Build a review step into the workflow: after the assistant produces a draft, run it through a checklist before publishing. The checklist can be a separate assistant, a shared document, or a prompt the editor runs manually. The review step is not optional for published content, regardless of how confident the draft looks.

At minimum, review for factual accuracy (especially statistics and attributed quotes), source availability (that cited sources exist and say what you attributed to them), and tone (that the voice brief was followed). Do not skip this step because the output looks polished.

A structured review is faster than an unstructured one. Write a short checklist for your most common content type: does the intro answer the question within the first two sentences, does each claim have a source, does the conclusion match what the article actually showed, and does the tone match the voice brief. Checking the same list each time builds consistent habits and catches the errors AI tools make most often.

Keep editorial decisions in sync across tools

A content workflow rarely runs through one AI app alone. Research might happen in one tool, drafting in another, editing in a third. If each tool has a different understanding of your voice rules and source requirements, the output from each step will conflict.

Storing your editorial rules in fenbs and connecting each tool via MCP means every assistant reads the same brief, regardless of which app it runs in. For an overview of which apps connect to fenbs, see /integrations.

How to do this with fenbs

1. Create a free account and add a project for the publication or content type.
2. Add a Skill with your full voice brief, source rules, and example inputs and outputs.
3. Add a Decisions and rules entry for any editorial choice that should not change between sessions.
4. Connect the AI apps in your workflow at /integrations so each one reads the same brief.
5. After each content round, add a Lessons learned entry if any rule was misread or a source problem slipped through.

For the mechanics of building the GPT itself, see how to build a custom GPT. For a comparison with Claude Projects, see Claude Projects vs. custom GPTs.