Big‑T Notation: Engineering for Token Efficiency in the Age of Enterprise AI
summary
Introduces Big‑T Notation as a practical metric decomposition — requests × models per request × agent depth — and shows concrete wins: database views and prefiltering cut calls by 92% or more, token-aware serialization yields 40–60% serialization savings, and some RAG/caching setups report >99% reductions for repeated queries. Recommends routing cheap models for routine tasks, treating code as the new code as cache layer to compress natural language, and avoiding vendor credit abstractions that hide per-token cost and block measurable optimization.
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chapters (8)
- 0:00Introduction
- 1:00The Token Cost Problem
- 4:02Big-T Notation Framework
- 5:56Code as Cache Layer
- 9:14Model Routing and Prompt Structure
- 11:53Token Caching and Vendor Transparency
- 14:08Layered Efficiency Stack
- 17:51Key Takeaways
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