AI Building-Block Prompting Model
Adapt proven prompts as modular components instead of copying them unchanged
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 94%
The building-block model treats prompts like modular Lego pieces. A useful prompt contributes structure—such as source selection, filtering, scoring, or output formatting—but should not be copied unchanged into a different organization or campaign. The user extracts the relevant component, edits its assumptions, adds local data and constraints, tests it on a bounded task, and refines it based on observed weaknesses. Complex workflows can then be assembled from several validated modules while high-effort creative outputs remain separated into dedicated prompts. This approach preserves learning across projects without mistaking a demonstration for a universal recipe. It also makes failures easier to diagnose because the practitioner can adjust the research, scoring, or asset-generation component independently instead of rewriting one opaque mega-prompt.
Origin
Extracted from Marketing Against The Grain as the host explains how viewers should reuse and customize the episode's prompts.
Core principles
- 01Treat shared prompts as starting components rather than finished solutions.
- 02Preserve useful structure while replacing assumptions and context.
- 03Break complex workflows into reusable modules.
- 04Refine modules from observed output quality.
- 05Recombine proven modules for new use cases.
How to run it
- 1
Extract the useful component
Identify which part of an existing prompt provides reusable value, such as a source filter, scoring rule, or output schema.
Pro tip Name the component by its function rather than the original project.
Watch out Do not carry over hidden assumptions simply because the example produced an attractive result.
- 2
Replace local assumptions
Adapt the company, product, audience, time range, sources, constraints, and terminology to the current use case.
Pro tip List every variable that should change between organizations or campaigns.
Watch out Copying proprietary, sensitive, or irrelevant context can create both quality and governance problems.
- 3
Compose the workflow
Combine compatible research, analysis, scoring, and output modules while preserving clear handoffs between them.
Pro tip Use structured outputs between modules.
Watch out Avoid building a single prompt with too many unrelated deliverables.
- 4
Test a bounded case
Run the adapted workflow on one representative input and inspect evidence, reasoning, and output quality.
Pro tip Compare the result against a manually reviewed baseline.
Watch out A fluent result is not proof that the underlying research or classifications are accurate.
- 5
Refine and preserve
Update the weak module, retest it, and save the validated version for future reuse.
Pro tip Version modules when changes affect their expected inputs or outputs.
Watch out Do not let a prompt library become a collection of unreviewed copies.
In the wild
A marketer copies the demonstrated ICP structure but replaces HubSpot-specific terminology, internal file seeds, buyer roles, external sources, and exclusion rules with evidence appropriate to a cybersecurity product. The output schema is retained because it supports the same downstream hunt.
→ The reusable structure survives while company-specific assumptions are removed.
After a campaign-kit prompt produces several mediocre assets, the marketer keeps its audience-research module but moves carousel, email, landing-page, and video generation into separate prompts with their own examples and rubrics.
→ The modular workflow produces stronger channel-specific work and is easier to improve.
Common mistakes
Copying and pasting unchanged
A prompt designed around another company's data, positioning, and constraints will reproduce assumptions that may not apply.
Building a mega-prompt
Combining too many different outputs makes weaknesses harder to diagnose and often lowers asset quality.
Saving unvalidated modules
Reusable components should earn their place through testing rather than being preserved because they sound sophisticated.
Is it for you?
Best for
Practitioners building repeatable AI workflows from examples, templates, and previously successful prompts.
Not ideal for
One-off tasks so simple that maintaining reusable prompt components would add unnecessary overhead.
From the transcript
“Everything I share is not meant to be for copy and paste. They're meant to be for build-in blocks.”
“I think of AI in some ways like Lego blocks.”
“And so you copy the things that look good, and then you edit refine to your needs.”
From the episode
How I Turned Perplexity Labs into a Marketing Machine