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Foundations

Self-paced · 3 modules · $299

Your prompts keep getting longer and your output keeps getting worse. This course teaches you the structural fix and how to keep your intent intact from start to finish.

You already know how to read a room: a client, a brief, an audience. Figuring out what something needs before you start is how you do your best work. This course teaches you to do the same thing with AI.

Written lessons, applied exercises on your own project, and weekly open office hours. Three modules, gradual release: demonstration, guided practice, independent practice. You finish with a working method built on your own real work.

Module 1

Task Decomposition

Demo, guided practice, independent practice

Your prompts keep getting longer and your output keeps getting worse. The problem is structural: compound instructions degrade in AI the same way they degrade in a classroom. You learn to run a room of specialists. Each one goes deeper than a compound prompt because its attention isn't split across five objectives at once. The AI gives you raw material at depth. You provide the synthesis: the calls the model cannot make because it doesn't have your judgment. You're directing.

Deliverable: A decomposed workflow for one real project, with before/after comparison.

Module 2

Input Inversion

Demo, guided practice, independent practice

Structured prompts compress what makes your thinking yours. Unstructured input (talking, brainstorming, arguing with yourself) contains richer raw material. You learn to dump your thinking and let the tool find the structure. The ideas that emerge are yours because the raw material was yours.

Deliverable: An input practice for one real project, with a recorded brain dump and its structured output.

Module 3

Voice Governance

Demo, guided practice, independent practice

All AI copy sounds the same because voice constraints are applied after generation, not during. You learn to extract how you actually talk (from conversation, not published writing), codify it as generation rules, and apply those rules before the first word is produced. The output sounds like you because the constraints occupied the space where the AI's defaults would have gone.

Deliverable: A voice protocol extracted from your own speech patterns, tested against real output.

What the difference looks like.

Brand design — task decomposition

Same brand. Same model. One compound prompt covering five dimensions at once. Five decomposed prompts, one per dimension. Here is what the logo direction came back as in each version.

Compound Output

Logo Direction

"A wordmark-forward logo using a hand-drawn or letterpress-style treatment with a supporting mark (possibly a stylized pepper or farm element). The mark should feel made by hand, not designed in software. Avoid flames, skulls, cartoons."

Decomposed Output

Logo Direction

"The mark should feel earned. It should carry the weight of someone who has done physical work to produce the thing in the jar. Drawn by someone with dirt under their fingernails, not in software. Reference sign painters' corner initials and printer's marks from the 1500s: compact enough to stamp, distinct enough to identify the shop. Reproducible by hand on a napkin. Must work at bottle-cap size and at eight-foot market-stall scale."

The compound version described what the logo looks like. The decomposed version described what it feels like, what it references, what it must avoid, and what constraints govern every scale. The practitioner now has something to act on, not just a spec to execute.

The methodology came from a fourth-grade classroom in Sunset Park, Brooklyn. Twelve students, twelve individualized education plans. A compound instruction ("take out your notebook, turn to page 42, answer questions three through seven") is three tasks arriving simultaneously. A student with processing delays hears the first one and starts. The second and third are gone.

The accommodation is decomposition: one instruction at a time, each one specific, each one completable before the next arrives. In that classroom, designing around the processing reality of the receiving system is federal law.

I transferred the framework to AI in 2022 and the fix was identical. A large language model processing a compound prompt distributes attention across all objectives at once. Each gets a fraction. Decompose the prompt and the model gives you depth it couldn't reach when its attention was split five ways.

That transfer is why the course has a name: Accommodation Design. The method predates AI by eight years. It was designed around a different receiving system entirely.

What's included

  • Gradual release pedagogy: demonstration, guided practice, independent practice (from IDEA-governed special education method)
  • Applied exercises on your own projects, not fictional examples
  • Three deliverables you use the day after
  • Frameworks, templates, and reference materials
  • Weekly open office hours. Bring your work or your questions. I work through real submissions from the group so you see the method applied to work like yours
  • Grounded in six published whitepapers with DOIs (citations include Sweller, Cowan, Murdock, Liu et al., Wong et al., Bock, Pennebaker, Flower & Hayes)

Built by a trained educator (Brooklyn College M.A. program, NYC Department of Education, Kingsborough Community College) using the same pedagogical framework the course teaches. The course structure itself demonstrates Accommodation Design. Read the research.

Foundations is complete on its own. For practitioners who want to go deeper, Advanced AI Practice courses (Input Inversion, Lens Extraction, Voice Governance, Semantic Hierarchy, and Coordinator Building) each deepen a single practice on your own real work. See all offerings.

$299

Three modules. Three deliverables. One methodology. Personal review from the founder.

Get on the list

After Foundations

After Foundations, Advanced Practice deepens the methodology and specialist tracks apply it to specific disciplines. Both are future offerings for graduates.