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Preparing your experience
Preparing your experience
A collection of named, battle-tested frameworks for building reliable AI systems at scale. Created from shipping AI products at Google, Meta, and in robotics. Used by thousands of practitioners.
Named frameworks create a common vocabulary. When your team says "Memory Budgeting," everyone knows exactly what patterns to apply.
These aren't theoretical. Each framework has been proven in production at scale—from Instagram Calling to warehouse robotics.
Every framework includes when to use it, how to measure success, and what failure modes to avoid. No hand-waving.
The essential patterns for building AI systems that actually work in production
Five patterns for building AI agents that work in production
How to manage context windows for agents that run for hours or days
How faster systems learn faster, creating a compounding advantage
More patterns for specific use cases
Three metrics that tell you if your RAG system is actually working
Bronze, silver, gold: a practical framework for AI safety in production
How to ship AI products without breaking things
1. Start with the problem. Each framework includes a "When to Use" section. Match your problem to the right framework.
2. Understand the failure modes. Every framework documents what goes wrong when you skip it or implement it poorly.
3. Instrument before optimizing. Each framework specifies exactly what to measure. Set up instrumentation first, then iterate.
4. Study the case studies. See how these frameworks were applied in real production systems at Google, Meta, and in robotics.
I teach these frameworks through workshops, talks, and consulting. I've helped teams at startups and Fortune 500 companies ship reliable AI products.