One-liner: Build a documented library of prompt patterns borrowed from 3+ different fields, with tested adaptations and transfer notes for your own domain.
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:
Name the technique
Describe the prompt pattern (what input, what instructions, what output format)
Why this technique works in this domain (what problem it solves)
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:
Core principle: What’s the underlying mechanism that makes this work, independent of domain?
Direct transfer: What would this look like applied to [your field] with minimal modification?
Modified transfer: What would need to change to make it work well in my context?
What doesn’t transfer: What aspect is domain-specific and should be replaced?
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:
Technique
Source Domain
My Task
Result Quality (1-5)
What Worked
What Needed Adjustment
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]
Survey 3 unfamiliar domains — Discover advanced AI techniques in three different fields. Cast a wide net — diversity of domains matters more than depth.
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.
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.
Test all 5 adaptations — Run each adapted prompt on a real task. Document quality, surprises, and adjustments needed.
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.
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.