Term tag: Context Window

Agent Memory

Agent memory is how an AI agent stores, retrieves, and summarizes information so it works across long tasks and sessions without overflowing its context window.

Context Engineering

Deciding what enters a model's context window each turn and what gets evicted — the discipline that took over from prompt engineering once agents arrived.

Context Window

Everything a model can see at once — why the advertised million tokens is not the usable number, and why you pay for the whole window on every turn.

Few-shot Learning (FSL)

Teaching a model a task by showing it a handful of worked examples. In its modern form, few-shot prompting, no weights are updated at all.

Large Language Model (LLM)

A large language model predicts the next token of text. How that produces answers, where its knowledge is stored, and why an LLM is not a chatbot.

Positional Encoding (RoPE)

Positional encoding tells a transformer where each token sits in a sequence. Why self-attention needs it, and how rotary embeddings (RoPE) work.

Prompt Caching

Repeated prompt prefixes bill at a fraction of the input price. How prefix matching works, what silently breaks it, and where the break-even sits.