Research Report 1.1: The Transformer Mechanism
Every AI product you use runs on the same core mechanism - a pattern-matching engine that processes entire sentences simultaneously instead of word by word, and understanding how it works changes how you build with it
A foundational deep dive into how transformer-based language models actually process information - covering the attention mechanism mathematics, multi-head attention specialization, positional encoding, tokenization, the quadratic complexity bottleneck, and the emergent behaviors that make modern AI systems both powerful and limited.
Also connected to
Twenty-three research reports generated from a single prompt framework - this is the template system that turned a research plan into a consistent, reproducible knowledge base
Every AI product you use runs on the same core mechanism - a pattern-matching engine that processes entire sentences simultaneously instead of word by word, and understanding how it works changes how you build with it
Documentation on claude 22 research report 7.2! performance & optimization
The project documentation for a 23-report research initiative that explains how LLM systems actually work - from transformer mechanics through multi-agent coordination, built for technical leaders who need accurate mental models rather than vendor marketing
How text becomes vectors, how similarity search works, and why vector databases are the backbone of semantic retrieval.