The Cross-Domain Prompt Library

One-liner: Build a documented library of prompt patterns borrowed from 3+ different fields, with tested adaptations and transfer notes for your own domain.

🔧 Jump in (Tinkerers start here)

You're going to build a personal prompt library of techniques stolen from other fields — documented well enough to teach someone else.

Phase 1 — Survey 3 domains. Pick 3 fields that are different from your own and from each other. Send this to three separate sessions:

How do professionals in [field] use AI in sophisticated ways? I don't want generic "they use ChatGPT" answers. Give me 5 advanced AI techniques or prompting patterns that are specific to this field. For each:

1. Name the technique
2. Describe the prompt pattern (what input, what instructions, what output format)
3. Why this technique works in this domain (what problem it solves)
4. Example prompt (ready to use)

Phase 2 — Select your top 5. From the 15 techniques across 3 domains, pick the 5 that are most interesting or most likely to transfer. For each one, send:

Analyze this technique from [source domain]: [technique description]

Map the transfer potential:

1. Core principle: What's the underlying mechanism that makes this work, independent of domain?
2. Direct transfer: What would this look like applied to [your field] with minimal modification?
3. Modified transfer: What would need to change to make it work well in my context?
4. What doesn't transfer: What aspect is domain-specific and should be replaced?
5. Adapted prompt: Write a ready-to-use version for my field

Phase 3 — Test each adapted prompt. Run all 5 adapted prompts on real tasks in your work. For each, document:

Phase 4 — Build the library entry. For the 3 best-performing techniques, create a library card:

Create a "Prompt Library Card" for this technique:

Name: [give it a memorable name] Borrowed from: [source domain] Core principle: [1 sentence — why this works] Original use: [what it does in the source domain] My adaptation: [what it does in my domain] Ready-to-use prompt:

[the tested, refined prompt with placeholders]

When to use: [scenarios where this technique is the right choice] When NOT to use: [scenarios where it fails or is overkill] Transfer notes: [what I learned about adapting this — tips for others]

📋 Plan first (Planners start here)

Here's what you're about to do:

1. Survey 3 unfamiliar domains — Discover advanced AI techniques in three different fields. Cast a wide net — diversity of domains matters more than depth.
2. Select the top 5 candidates — From 15 techniques, choose 5 based on transfer potential and novelty. Look for techniques that solve a problem structurally similar to one in your work.
3. Analyze transfer mechanics — For each technique, separate the domain-specific elements from the core principle. Identify what transfers directly, what needs modification, and what should be replaced.
4. Test all 5 adaptations — Run each adapted prompt on a real task. Document quality, surprises, and adjustments needed.
5. Document the top 3 — Create library cards with enough detail for someone else to use the technique without your guidance.

"Done" looks like: A 3-entry prompt library with tested techniques from other domains, complete with ready-to-use prompts, usage guidance, and transfer notes.

🧭 Why this matters (Strategists start here)

In CDR-Basic-01, you borrowed a single technique. In CDR-Intermediate-01, you transplanted an entire framework. Here, you're building a systematic practice — a personal library that compounds over time. The library card format forces you to articulate why a technique transfers, which is the meta-skill: once you can spot the structural similarity between domains, you can generate new cross-domain adaptations on your own. This library also becomes a shareable team asset — a collection of non-obvious AI techniques that others in your field won't have discovered.

Reflection

• Which of the 3 domains produced the most transferable techniques? Why?
• Did any technique work better in your domain than in its original domain? What does that tell you?
• What pattern do you notice in what transfers well vs. what doesn't? Can you articulate a rule of thumb?
• 💬 Share your prompt library with colleagues and have them test the techniques on their own tasks. The techniques that work across multiple people's contexts are genuinely domain-agnostic — those are your keepers. (Social Learners)

⬆️ Level up

You've reached the advanced level for Cross-Domain Reframing. From here, consider:

• Expanding the library monthly — add one new cross-domain technique per month from a new field
• Sharing the library with colleagues and collecting their transfer notes
• Combining this with WA-Advanced-01 to build cross-domain techniques into automated workflows

Back to Cross-Domain Reframing