When I started building with LLMs, I kept running into terms I didn't fully understand. Quantization, KV cache, top-k sampling, temperature. Every time I looked one up, I got either a textbook definition or a link to a paper.

That told me what the term is . It didn't tell me what to do with it. What decision does it affect? What breaks if I ignore it? What tradeoff am I making?

So I started keeping notes. For each term, I wrote down the production angle: why it matters when you're actually shipping something. Over time it grew into 30+ entries organized across 8 pillars, from Core Architecture to Agentic AI, with linked related concepts so you can follow threads naturally.

I cleaned it up, built a browsable UI with search and filtering, and open sourced it.

tomerjann / llm-field-notes

LLM terms explained from an engineering perspective with the production implications, not just the definition.

llm-field-notes

LLM terms explained from an engineering angle, with the production implications, not just the…