mattpocock/dictionary-of-ai-coding
mattpocock/dictionary-of-ai-codingAI coding jargon, explained in plain English.
From the README
AI Coding Dictionary
AI coding can feel like it's just for experts. Unexplained jargon. Mysterious failures. Bills that don't seem to match the work.
It isn't, really. A lot of the confusion is manufactured: there's a whole VC-funded economy that benefits from keeping it hard to understand.
The basic terms of engagement are learnable in an afternoon. Once you have them, the whole thing stops feeling like guesswork.
Why does context degrade? Why is the bill so high? Why does the same prompt behave differently from one day to the next?
Each has a clean answer, once someone tells you the words to use.
That's what this dictionary is for. The vocabulary of AI coding, translated into plain English.
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Table of contents
Section 1 — The Model
- Model
- Parameters
- Training
- Inference
- Token
- Next-token prediction
- Non-determinism
- Model provider
- Harness
- Model provider request
- Input tokens
- Output tokens
- Prefix cache
- Cache tokens
Section 2 — Sessions, Context Windows & Turns
Section 3 — Tools & Environment
- Environment
- Filesystem
- Tool
- Tool call
- Tool result
- Permission request
- Permission mode
- Agent mode
- Sandbox
Section 4 — Failure Modes
- Sycophancy
- Hallucination
- Parametric knowledge
- Knowledge cutoff
- Contextual knowledge
- Attention relationship
- Attention budget
- Attention degradation
- Smart zone
Section 5 — Handoffs
Section 6 — Memory and Steering
Section 7 — Patterns of Work