AI glossary: my cheat sheet
The AI terms from the series in one place, grouped by topic, each with a one-line definition and a link to the post that explains it: tokens, RAG, tools, MCP, agents, LoRA, evals and more.
Tag: LLM
10 entries
How a language model is built
Transformers, attention, pretraining, fine-tuning and preference training (RLHF, DPO): the stages that turn a pile of text into a chat assistant.
Running models locally: Ollama and quantization
Why run an open-weight model on your own machine, how much memory it needs, what quantization does, and how to call a local model with Ollama, including from the Anthropic SDK.
Multimodal: images and PDFs
Sending images and PDFs to a model, how image tokens are counted, what vision is still bad at, and what to do with audio.
Temperature, top-p and hallucinations
How the next token is picked, what temperature and top-p do, why current Claude models no longer accept them, and why models hallucinate.
Prompting: how to ask a model
The system prompt, clear instructions with a reason, few-shot examples, XML tags and long documents: the prompting techniques the official guide recommends.
Reasoning models: thinking before answering
What thinking is, how adaptive thinking and the effort parameter control it, how to read thinking blocks, and what it costs.
Structured outputs and streaming
Get JSON that always matches your schema with structured outputs and Pydantic, and show the answer while it is being written with streaming.
Tokens and the context window
What a token is, how to count tokens before you send a request, what fills the context window, and what happens when it runs out.
What is an LLM
A large language model predicts the next token, over and over. What that means, what it gets you, and your first API call to one in Python.