I created a new training dataset for stale SOTA LLMs

I ran into a common problem building with Apple's new iOS 26 Foundation Models framework. Every major LLM (GPT-4, Claude, Gemini) knows nothing about it due to training cutoffs.

Rather than wait months for model updates, I spent 2 hours building custom training data:

Research: Used Gemini's deep research to crawl all available docs, forums, GitHub repos, Reddit threads, YouTube transcripts. Looking for "iOS 26 Foundation Models Framework".

Optimization: Had Claude restructure everything into clean, hierarchical markdown optimized for LLM ingestion.

Implementation: Loaded into Claude Projects as a custom knowledge layer.

Result: Went from "I don't have information about that" to expert-level guidance on bleeding-edge APIs. My development workflow shifted from trial-and-error to smooth AI-assisted implementation.

The research was so thorough it even referenced my own Apple dev forum post from the day before—creating a weird recursive loop where I was training AI on knowledge I'd just contributed.

This approach works for any new framework or API. The pattern is predictable: every major release creates a temporary knowledge gap that custom training data can fill.

Technical writeup with methodology: https://rileygersh.medium.com/how-i-gave-claude-gemini-knowledge-of-ios-26s-foundation-models-03395d7e905c