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Prompting with frameworks the model already knows

Structure your prompts the way a consultant structures a brief, then borrow a framework the model was trained on. You give it a running start instead of describing every step.

3 min read

Talking to a model is like briefing someone with no memory and no context. When you talk to a colleague, you supply background, explain what you want, and pitch your language to them. A model needs the same, made explicit. That’s what structured prompting is.

Structure with MECE

I’m not usually one for acronyms, but MECE - Mutually Exclusive, Collectively Exhaustive - earns its place. Break the prompt into distinct, non-overlapping parts that together cover everything the model needs:

  1. Context - the background and the scene
  2. Goals - what you’re trying to achieve
  3. Audience - who it’s for
  4. Style and tone - how it should read
  5. Rules - specific dos and don’ts
  6. Additional information - anything that doesn’t fit elsewhere

This isn’t about tidiness. It’s a roadmap: each part tells the model something it needs and nothing it doesn’t.

Front-load the important context

A useful quirk: many models weight information that appears earlier in the prompt more heavily. If you have context that’s critical to the task, put it up front rather than burying it at the end. Anthropic’s documentation has a strong prompt-engineering course worth working through.

Models also handle structure well, so XML-style tags can help them parse a document and let you reference sections precisely in follow-ups:

<document>
  <section name="overview">This is the overview.</section>
  <section name="features">A list of features.</section>
  <section name="pricing">Pricing information.</section>
</document>

Borrow a framework the model knows

Models are trained on enormous amounts of text, including well-known frameworks. Use that. Framing your request inside a framework the model already understands gives it a running start - like asking a chef for a specific dish rather than describing every ingredient and step. The model spends its effort on the unique parts of your request, not on reconstructing the structure.

Ask for a business analysis and reference SWOT, and the model already knows the shape; it can go straight to applying it to your case.

A worked example

Suppose you want help drafting a post on sustainable fashion:

Context: A fashion blogger writing about sustainable fashion for an
         eco-lifestyle site.


Goals:
1. Explain the environmental impact of fast fashion.
2. Give practical tips for building a sustainable wardrobe.
3. Highlight innovative sustainable brands.


Audience: Environmentally conscious millennials interested in fashion.
Style & Tone: Conversational and informative. British English.


Rules:
- Avoid jargon.
- Include at least three actionable tips.
- Mention no more than five brands.


Additional Information:
- Use the "4 Rs" framework (Reduce, Reuse, Recycle, Reimagine) as the
  main sections.
- Include a short section on circular fashion.

The model knows who it’s writing for, what you want, and - via the “4 Rs” - a familiar structure to hang the content on.

The bottom line

Effective prompting is clear communication. Structure your input and borrow a known framework, and you make the model’s job easier and your output better. A model is a tool, not a mind reader. The clearer and more structured the input, the better the result - it’s closer to programming than to conversation.