Explain the difference between few-shot learning, zero-shot learning, and chain-of-thought prompting.

Zero-shot prompting gives only the instruction; few-shot prompting adds a handful of worked examples of the task; chain-of-thought prompting asks the model to reason through intermediate steps before giving the answer. The first two control what the model sees about the task; the third controls how it produces the answer, and it can be combined with either.

Zero-shot Only the task, with no examples:

Classify the sentiment of this review as positive, negative or mixed.
Review: "Fast delivery, but the charger stopped working after a week."

• Quickest to write and shortest to send.
• Works well for common tasks that modern instruction-tuned models already understand.
• Less reliable when the format, labels or judgement are specific to you.

Few-shot A few input and output pairs before the real input:

Review: "Arrived broken." -> negative
Review: "Exactly as described, great value." -> positive
Review: "Lovely screen, terrible battery." -> mixed
Review: "Fast delivery, but the charger stopped working after a week." ->

• The model infers the pattern, labels and format from the examples; Brown and colleagues popularised the term in 2020, showing large models could learn tasks from examples in the prompt without retraining.
• Pick examples that cover the hard cases and vary in form, or the model copies superficial features such as length or wording.
• Costs more tokens on every call.

Chain-of-thought (CoT) The model writes out intermediate reasoning before the final answer. Wei and colleagues showed that examples containing reasoning steps improved arithmetic and logic results in large models; Kojima and colleagues found that simply adding "Let's think step by step" (zero-shot CoT) helped too.

A shop sells pens in packs of 12. A school needs 150 pens.
How many packs must it buy? Work through it, then give the answer on the last line.

• Helps multi-step maths, logic and planning; adds little for simple lookups or classification.
• Costs more output tokens and time.
• The written reasoning is not a guaranteed account of how the model reached the answer.

How this has changed Reasoning models now think through problems internally before answering. Their makers advise simple, direct prompts for them, and say that telling them to "think step by step" is unnecessary and can even hurt. CoT prompting still matters for models without built-in reasoning.

In practice Start zero-shot; add examples when the format or judgement isn't right; ask for step-by-step reasoning (or use a reasoning model) when the task needs several steps of logic.