Collaborative Teaching of Frontier Technologies to Advanced AI

Steve Repetti

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Collaborative Teaching of Frontier Technologies to Advanced AI<br>A funny thing happened on the way to teaching advanced AI core frontier tech.

Steve Repetti<br>May 19, 2026

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A funny thing happened on the way to teaching advanced AI core frontier tech.<br>I set out to teach Claude some advanced features of FGL (Fifth-Generation Language and Platform that I co-authored). I wanted an AI that could understand its frontier aspects and provide assistance to others using it — particularly as it relates to the complexities of law enforcement, AI, and other mission critical aspects. AI had this annoying trait of confabulating React-shaped answers when asked about something that had nothing to do with React. And it worked! The AI got fluent. The collaboration compounded.<br>Along the way I noticed something I had not expected. The process itself became the vehicle for the solution. The collaboration, the iterative learning for both me and for Claude. The thing I was building was not really an AI that knew my platform. It was a file — a portable, plain-text file — that, when handed to any modern AI, made it fluent in the platform in minutes. Hand the file to a fresh Claude session: fluent. Hand it to ChatGPT: fluent. Hand it to Gemini: fluent. The fluency was in the file, not the model. The model was just the reader.<br>That’s the thing this paper is about. The artifact, the practice, the process — and how to build one for whatever unique knowledge you happen to have.<br>Editorial note. This paper was produced inside the loop it documents. Authorship is shared. It is my story – I came into the room with it and set the scope, the voice, the direction – but it was also augmented and enhanced by my collaborator Claude — Anthropic’s frontier AI. Together, it became our story. Every word has both our signatures on it.<br>What this is, and what it means for you

A portable primer is a single file that, when dropped into any modern AI chat, makes that AI fluent in a specific body of knowledge for the rest of the session. The file is plain content — Markdown, plain text — that any AI can read natively. It is highly optimized for AI, and it is acutely aware of the constraints of size, context window, and token costs. There is no installation, no account, no platform, no configuration; the optimization is in the primer’s structure, which the paper unpacks later. You drag the file into the chat, hit enter, and the AI is fluent.<br>That property is what makes the primer different from everything else in the toolbox. No setup. No install. No account. Any computer, any AI: the file is the toolchain.<br>A portable primer is, literally, domain-specific transcendence — in a single readable file.

And that is one primer. Wait until you start stacking them — frontier tech plus project-specific plus regulatory context, loaded together. The AI is fluent in the intersection — no single primer reaches that alone.<br>Where this fits alongside Skills

Several platforms now offer knowledge containers — Skills, Gems, Custom GPTs, Notebooks, and similar — where a user configures an AI with instructions and source files inside that platform’s ecosystem. These are real tools for what they do. They are also platform-bound: the container lives in one vendor’s product, requires an account, and only works inside that vendor’s AI.<br>A portable primer occupies a different point on the same map. It is a single file that works in any AI, on any computer, with no platform involvement. That makes it the right shape for a different set of cases: knowledge that has to travel, knowledge whose users span multiple AI tools, knowledge being shared informally, knowledge being refined through use before it is ready to be locked into a platform.<br>The two are also complementary. A primer is an excellent on-ramp to a Skill — write the primer first, refine it through use, then convert the mature version into a Skill when you are ready to commit to a platform-deployed deliverable. The primer is portable and fast to iterate; the Skill is platform-deployed and structurally richer. Different stages of the same arc.<br>The rest of this paper is about the portable-primer half of that arc: how to recognize knowledge that benefits from being packaged this way, how to build a good primer, how to use it, how to keep it useful over time, and how to share it.<br>Who this is for

The highest-return use cases are frontier work — domains where the practitioner’s knowledge runs ahead of what the AI has been trained on. The AI has not seen this work at scale. Its default output is the nearest analog from its training data, and that analog does not match the actual domain. A primer is the most direct fix: encode what the AI does not yet know, load it at the start of the session, work from there.<br>Frontier work is where the practitioner knows more than the model. The primer hands the model the missing half.

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