Zero Shot vs Few Shot Prompting: Key Differences and Usage

In zero shot prompting, a model answers tasks without any examples, relying only on the prompt instructions. In few shot prompting, the model is given a few examples in the prompt to guide its responses, improving accuracy on similar tasks.

Quick Comparison

This table summarizes the main differences between zero shot and few shot prompting.

Factor	Zero Shot Prompting	Few Shot Prompting
Definition	No examples given, only instructions	Includes a few examples with instructions
Prompt Length	Shorter prompts	Longer prompts due to examples
Model Guidance	Relies on model's general knowledge	Guided by examples to shape output
Accuracy	May be lower on complex tasks	Usually higher with relevant examples
Use Case	Quick, general tasks or unknown domains	Tasks needing specific style or format
Cost	Less token usage, cheaper	More token usage, costlier

Key Differences

Zero shot prompting means you ask the AI to perform a task by giving only the instructions, without showing any examples. The model uses its training knowledge to understand and answer the task. This is like asking a friend a question without giving them any hints or samples.

Few shot prompting adds a few examples in the prompt before the actual question. These examples show the model how to respond, which helps it produce better and more accurate answers. It's like showing your friend a couple of solved problems before asking them to solve a similar one.

The main difference is that few shot prompting provides context and guidance through examples, improving performance especially on tasks that need specific formats or reasoning. Zero shot is faster and cheaper but can be less precise on complex tasks.

Code Comparison

Here is how zero shot prompting looks using OpenAI's GPT API in Python to classify sentiment without examples.

python

import openai

openai.api_key = 'your-api-key'

prompt = "Classify the sentiment of this sentence: 'I love sunny days!'"

response = openai.Completion.create(
    model='text-davinci-003',
    prompt=prompt,
    max_tokens=10
)

print(response.choices[0].text.strip())

Output

Positive

Few Shot Prompting Equivalent

Here is the few shot version where we provide examples before the task.

python

import openai

openai.api_key = 'your-api-key'

prompt = ("Classify the sentiment of the sentences below.\n"
          "Sentence: 'I am very happy today.'\nSentiment: Positive\n"
          "Sentence: 'This is so frustrating.'\nSentiment: Negative\n"
          "Sentence: 'I love sunny days!'\nSentiment:")

response = openai.Completion.create(
    model='text-davinci-003',
    prompt=prompt,
    max_tokens=10
)

print(response.choices[0].text.strip())

Output

Positive

When to Use Which

Choose zero shot prompting when you need quick answers without extra context, or when you don't have example data ready. It's good for general questions or when cost and prompt length matter.

Choose few shot prompting when your task requires specific formats, styles, or higher accuracy. Providing examples helps the model understand exactly what you want, especially for complex or niche tasks.

Key Takeaways

Zero shot prompting uses only instructions, no examples, for quick and general tasks.

Few shot prompting includes examples to guide the model, improving accuracy.

Few shot prompts are longer and cost more tokens but yield better results on complex tasks.

Use zero shot for fast, broad queries; use few shot when task precision matters.

Providing examples helps the model learn your desired output style on the fly.