dictionary-of-ai-coding

What this skill does

```markdown
---
name: dictionary-of-ai-coding
description: AI coding jargon dictionary with plain English explanations of models, tokens, agents, context windows, and more
triggers:
  - what does context window mean in AI coding
  - explain AI coding terminology
  - what is a token in AI
  - explain agent mode and tool calls
  - what does hallucination mean for AI
  - AI coding jargon explained
  - what is a harness in AI coding
  - explain prefix cache and cache tokens
---

# Dictionary of AI Coding

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

**mattpocock/dictionary-of-ai-coding** is a plain-English reference glossary for AI coding terminology. It explains the vocabulary behind models, tokens, agents, context windows, failure modes, memory systems, and workflow patterns — without assuming prior ML expertise.

---

## Installation

```bash
# Clone the repo
git clone https://github.com/mattpocock/dictionary-of-ai-coding.git
cd dictionary-of-ai-coding

# Install dependencies
npm install

# Generate the README from source markdown files
npm run generate
```

### Project Structure

```
dictionary-of-ai-coding/
├── dictionary/          # Individual term .md files (source of truth)
├── internal/
│   ├── Curriculum.md    # Section ordering and term grouping
│   └── README.template.md  # README template
├── README.md            # GENERATED — do not edit directly
└── package.json
```

> **Important:** `README.md` is auto-generated. All term definitions live in `dictionary/*.md`. Edit those files, then run `npm run generate`.

---

## Core Concepts Quick Reference

### Section 1 — The Model

| Term | Plain English |
|------|--------------|
| **Model** | The parameters. Stateless — does next-token prediction and nothing else. |
| **Parameters / Weights** | Billions of numbers tuned during training. Everything the model "knows." |
| **Training** | One-time process by the provider that sets the parameters. |
| **Inference** | Running the model to generate output. Billed per token. |
| **Token** | Atomic unit — roughly word-sized. Cost, latency, context size all in tokens. |
| **Next-token prediction** | All the model does: sample one token, append, repeat. |
| **Non-determinism** | Same input → different output. No setting eliminates this. |
| **Model provider** | Service that runs inference (Anthropic, OpenAI, Ollama locally). |
| **Harness** | Everything around the model: tools, system prompt, permissions. |
| **Model provider request** | One round-trip from harness to provider. Tool calls spawn many. |
| **Input tokens** | Tokens sent to the model. Billed at lower rate. |
| **Output tokens** | Tokens the model generates. Billed ~5× higher than input. |
| **Prefix cache** | Provider-side cache of shared request prefixes — much cheaper tokens. |
| **Cache tokens** | Input tokens reused from cache; billed at reduced rate. |

### Section 2 — Sessions, Context & Turns

| Term | Plain English |
|------|--------------|
| **Stateless** | Model has no memory between requests. The harness provides all context. |
| **Context** | Everything sent to the model in a single request. |
| **Context window** | Maximum tokens the model can process at once. |
| **Stateful** | An agent that maintains history across turns (via the harness). |
| **Agent** | A harnessed model that loops: plan → tool call → observe → repeat. |
| **System prompt** | Instructions prepended before user messages. Part of input tokens. |
| **Session** | One continuous thread of turns in a harness. |
| **Turn** | One user message + one model response cycle. |

### Section 3 — Tools & Environment

| Term | Plain English |
|------|--------------|
| **Environment** | Everything the agent can read/write: filesystem, shell, APIs. |
| **Filesystem** | Files the agent can read and edit via tools. |
| **Tool** | A function the model can call by emitting structured output. |
| **Tool call** | The model's structured request to invoke a tool. |
| **Tool result** | Data returned to the model after a tool runs. |
| **Permission request** | Prompt asking the user to approve a tool action. |
| **Permission mode** | How strictly the harness gates tool use (ask/auto/deny). |
| **Agent mode** | Harness config enabling autonomous multi-step tool use. |
| **Sandbox** | Isolated environment limiting what tools can affect. |

### Section 4 — Failure Modes

| Term | Plain English |
|------|--------------|
| **Sycophancy** | Model agrees with you instead of being accurate. |
| **Hallucination** | Model generates plausible-sounding but false information. |
| **Parametric knowledge** | What the model learned during training. May be outdated. |
| **Knowledge cutoff** | Date after which training data stops. Model doesn't know newer things. |
| **Contextual knowledge** | Information loaded into context for this session only. |
| **Attention relationship** | How strongly the model connects two parts of context. |
| **Attention budget** | Finite capacity to maintain relationships across the context window. |
| **Attention degradation** | Quality drops when context is too long or key info is buried. |
| **Smart zone** | Region of context where attention is strongest (typically start/end). |

### Section 5 — Handoffs

| Term | Plain English |
|------|--------------|
| **Clearing** | Ending a session to reset context before starting fresh. |
| **Handoff** | Transitioning work between sessions or agents with a summary. |
| **Handoff artifact** | Document capturing state for the next session. |
| **Spec** | Written description of what to build, used as context. |
| **Ticket** | Discrete unit of work described well enough for an agent. |
| **Compaction** | Summarising history to free context window space. |
| **Autocompact** | Harness-triggered compaction when context nears the limit. |

### Section 6 — Memory & Steering

| Term | Plain English |
|------|--------------|
| **Memory system** | How the harness persists information across sessions. |
| **AGENTS.md** | File the harness injects as standing instructions (like system prompt). |
| **Progressive disclosure** | Loading context incrementally rather than all at once. |
| **Skill** | Reusable block of context/instructions for a specific task type. |
| **Subagent** | Agent spawned by another agent to handle a subtask. |

### Section 7 — Patterns of Work

| Term | Plain English |
|------|--------------|
| **Human-in-the-loop** | Workflow where a human reviews/approves agent actions. |
| **AFK** | Running the agent unattended without human oversight. |
| **Automated check** | Machine-verifiable test the agent can run itself (lint, types, tests). |
| **Automated review** | CI/CD pipeline that validates agent output. |
| **Human review** | A person inspecting agent output before it ships. |
| **Vibe coding** | Accepting agent output without deep review. High speed, higher risk. |
| **Design concept** | High-level intent shared with the agent before implementation. |
| **Grilling** | Interrogating the model's reasoning to surface hidden assumptions. |

---

## Adding or Editing Terms

Each term is a standalone Markdown file in `dictionary/`:

```markdown
<!-- dictionary/my-new-term.md -->
### My New Term

Plain-English definition here. Reference related terms with [brackets](#term-anchor).

*Usage:*

"Question someone might ask?"

"Answer that uses the term naturally."
```

Then register it in `internal/Curriculum.md` under the right section:

```markdown
## Section N — Section Name

- My New Term
```

Regenerate:

```bash
npm run generate
```

---

## Common Patterns & Troubleshooting

### Why is my bill so high?

**Root cause checklist:**

1. **Output tokens** — Agent rewriting whole files. Output is ~5× more expensive than input. Instruct it to emit diffs/patches instead.
2. **No prefix cache hit** — Something changes early in the context each turn (timestamps, random IDs). Move dynamic content to the end of the system prompt.
3. **Tool call explosion** — One user me