Zero-Shot vs Few-Shot vs Chain-of-Thought Prompting

These three terms get thrown around a lot in AI articles. They sound technical but the ideas are simple: how many examples do you give the AI, and do you ask it to think out loud? This tutorial demystifies all three and shows you exactly when each one shines.

1. Introduction

There is no single "right" way to prompt. Some tasks need a quick instruction. Some need examples. Some need the AI to slow down and reason. Knowing which style to reach for is how you go from a casual user to a confident one.

2. The Concept Explained

Zero-Shot Prompting

Zero examples in the prompt. You just tell the AI what to do and let it figure things out from its existing knowledge. This works best for tasks the model has seen many times during training — translation, summarisation, classification of common things, basic Q&A.

Few-Shot Prompting

A small number of examples — typically two to five — showing the input and the kind of output you want. Then you give a fresh input and ask the model to follow the same pattern. This is the easiest way to teach the AI a specific style, tone, or output structure that it would otherwise not produce on its own.

Chain-of-Thought (CoT) Prompting

Ask the model to show its reasoning step by step before giving a final answer. The simplest version is to add the phrase

Let's think step by step.

or "Walk through your reasoning before answering." This dramatically improves accuracy on tasks that require logic, maths, or multi-step decisions.

3. The Problem with Picking the Wrong Style

If you use Zero-Shot for a task that needs reasoning, the AI tends to jump straight to an answer — often wrong. If you use Few-Shot but give bad or inconsistent examples, the model will copy your inconsistencies. If you use Chain-of-Thought for a simple translation, you waste tokens and time. Picking the right style is half the battle.

4. The Three Styles in Action

Zero-Shot example

Classify the sentiment of this review as positive, negative, or neutral.

Review: "The food was great but the service made me never want to come back."

The model has seen thousands of sentiment-classification tasks. It will respond with a label without needing any examples.

Few-Shot example

Convert product names into a friendly two-line description.

Product: Bluetooth Earbuds
Description: Pop them in, walk out the door, and your music goes with you. Light, wireless, and ready when you are.

Product: Insulated Steel Water Bottle
Description: Cold drinks stay cold for hours. Hot drinks stay hot. The bottle does the work so you don't have to.

Product: Foldable Travel Backpack
Description:

The AI sees the pattern: short, friendly, two lines, present tense, ends warmly. It will produce a new description in exactly that style.

Chain-of-Thought example

A library has 240 books. 30% are fiction. Of the fiction books, half are
mystery novels. How many mystery novels does the library have?

Think through it step by step before giving the final answer.

The model will write out: 30% of 240 = 72 fiction books. Half of 72 = 36 mystery novels. The reasoning makes the answer trustworthy and easy to check.

5. Step-by-Step: Picking the Right Style

1. Is the task common and obvious? (translate, summarise, classify) → Start with Zero-Shot.
2. Do you need a very specific style, tone, or output format? → Use Few-Shot. Give two or three clear examples first.
3. Does the task involve reasoning, maths, or multi-step logic? → Add Chain-of-Thought. "Think step by step before answering."
4. Is the task hard and needs a specific format? → Combine Few-Shot and Chain-of-Thought. Show examples that include reasoning.
5. Always iterate. If Zero-Shot fails, try Few-Shot next, not "more words".

Note: Modern reasoning models (like recent versions of GPT and Claude) often "chain-of-thought" automatically behind the scenes. You may not always need to ask for it explicitly — but on tasks that look mathematical or multi-step, doing so still helps.

6. Practice Exercises

7. Key Takeaways