Term tag: Prompt Engineering

Chain-of-Thought (CoT)

A model writing out its reasoning before answering. Once a prompting trick, now trained in by RL, billed as output tokens, and frequently unfaithful.

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.

LLM-as-a-Judge

Using one model to grade another's output against a rubric: how it works, the biases that break it, and the human agreement rate you can actually reach.

One-shot Learning

Learning a new class from exactly one example — and the split that matters: one-shot prompting updates no weights; classical one-shot training does.

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.

Prompt Engineering

Writing the input to a fixed, already-trained language model so it reliably produces the output you want — steering the model, not retraining it.

Zero-shot Learning

Performing a task with zero examples, on prior knowledge alone — and the split that matters: zero-shot prompting versus classical zero-shot learning.