AI Foundational Layer
Ground every AI skill in shared, reusable business context.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 5
- Confidence
- 99%
The AI Foundational Layer is a shared context system placed beneath individual prompts, skills, and workflows. Instead of encoding all organizational knowledge separately inside every skill, a team maintains concise Markdown files describing its identity, working style, audience, market position, and customer journey. Relevant skills load this intelligence before producing content or strategic work. The mechanism separates capability from context: a skill defines how to perform a task, while the foundation supplies the distinctive information needed to perform it for this particular organization. Because many skills depend on the same files, one carefully grounded update can improve numerous outputs. The files should remain concise, non-overlapping, actively maintained, and selectively loaded to avoid confusing the model.
Origin
Extracted from Marketing Against The Grain, where the host applies Pixar's shared Brain Trust concept to AI marketing systems and calls the resulting context system a foundational layer.
Core principles
- 01Context quality constrains the quality of every skill built above it.
- 02Reusable intelligence should live below individual prompts and workflows.
- 03Foundational files should describe the organization, audience, market, and buying journey.
- 04Improving shared context should improve many downstream skills at once.
- 05Short complementary files are safer than a large mass of overlapping instructions.
How to run it
- 1
Inventory repeated context
Identify information repeatedly needed across marketing skills, such as audience language, voice rules, positioning, and buying behavior.
Pro tip Start with context that affects several outputs rather than information needed by only one task.
Watch out Do not mistake a collection of task prompts for a reusable foundation.
- 2
Create the foundation
Make a dedicated folder containing concise core Markdown files. Give each file one clear contextual responsibility.
Pro tip Begin with the four starter files described in the episode.
Watch out A single oversized document can overload the model and make instructions difficult to route.
- 3
Remove overlap
Review the files together and ensure that each contributes distinct, complementary guidance.
Pro tip Assign every rule to the one file where it naturally belongs.
Watch out Conflicting or duplicated instructions can produce inconsistent AI behavior.
- 4
Connect skills to context
Make each skill inspect the foundation and load the files relevant to its current task.
Pro tip Declare explicit loading and exclusion conditions in every foundational file.
Watch out Loading every file for every task can confuse the model.
- 5
Maintain the layer
Refresh the foundation as the audience, company, market, and observed performance change.
Pro tip Schedule a quarterly review and add evidence from actual output performance.
Watch out A stale foundation can consistently reproduce outdated assumptions across many skills.
In the wild
A team creates separate files for audience delight, creator style, market positioning, and customer journey intelligence. Its newsletter, social-post, landing-page, and sales-enablement skills load the relevant subset before generating work.
→ Outputs become more consistent and specific without rewriting every individual skill.
A company finds that twelve AI workflows each contain slightly different descriptions of its target buyer. It moves the authoritative buyer language into one foundational file and updates each workflow to retrieve it when needed.
→ Audience assumptions remain aligned and can be updated in one place.
Common mistakes
Optimizing skills before context
Repeated prompt and model refinements deliver only marginal gains when the skill still lacks distinctive source context.
Creating overlapping files
Duplicated responsibilities expose skills to mixed or conflicting instructions.
Treating the layer as static
The foundation loses value when it is not refreshed as the market and evidence change.
Is it for you?
Best for
It is best for teams using multiple AI skills that should consistently reflect the same audience, voice, positioning, and customer intelligence.
Not ideal for
It is not ideal for isolated factual tasks that require no organization-specific context.
From the transcript
“I call this for us the foundational layer.”
“Every single system you build, and when you are building skills for anything you want to do to grow your business in a post-AI world,…”
“And in that layer are core.md files that help describe who you are, how you work, and who your audience is and what they react…”
From the episode
The Real Reason Your AI Content Is Average (It's Not Your Prompts)