Zero-Shot vs Few-Shot Prompting: When to Use Each
One of the most powerful yet underutilized techniques in prompt engineering is few-shot prompting—providing examples to guide AI responses. Understanding when to use examples and when to skip them can dramatically improve your results. This guide explains the difference between zero-shot and few-shot prompting, shows you how to implement each effectively, and helps you choose the right approach for any task.
What Is Zero-Shot Prompting?
Zero-shot prompting is when you ask an AI model to perform a task without providing any examples. You simply describe what you want, and the model uses its training to figure out how to respond. It's the most straightforward approach and what most people use by default.
Prompt: "Classify the sentiment of this review as positive, negative, or neutral: 'The product arrived quickly but the quality was disappointing.'"
What happens: The AI uses its general understanding of sentiment analysis to provide an answer without seeing any examples of how you want it done.
Zero-shot prompting relies entirely on the model's existing knowledge and understanding of language. For many common tasks, this works perfectly well. Modern AI models have been trained on vast amounts of text and can handle a wide variety of requests without explicit examples.
Zero-shot prompting is like asking someone to do something they already know how to do—you trust their existing knowledge and skills to complete the task correctly.
What Is Few-Shot Prompting?
Few-shot prompting involves providing one or more examples of the task you want completed before asking the AI to perform it. These examples show the model exactly what format, style, or approach you expect. The AI learns from these examples and applies the same pattern to your actual request.
Prompt: "Classify the sentiment of these reviews as positive, negative, or neutral:
Review: 'Amazing product! Exceeded all my expectations.'
Sentiment: Positive
Review: 'Terrible quality. Broke after one use.'
Sentiment: Negative
Review: 'It's okay. Does what it's supposed to do.'
Sentiment: Neutral
Review: 'The product arrived quickly but the quality was disappointing.'
Sentiment:"
What happens: The AI sees your pattern and follows it, providing a consistent answer in the same format.
Few-shot prompting is remarkably effective because AI models excel at pattern recognition. When you show them examples, they quickly adapt to match your specific requirements, even for unusual or custom tasks that weren't part of their training data.
How Many Examples Do You Need?
The term "few-shot" typically means 2-5 examples, though you can use anywhere from one example (one-shot) to dozens. More examples generally mean better consistency, but there are diminishing returns. For most tasks, 2-4 well-chosen examples are sufficient.
When to Use Zero-Shot Prompting
For Standard, Well-Defined Tasks
When asking for common tasks that AI models handle regularly—like summarization, translation, basic writing, or answering straightforward questions—zero-shot prompting works excellently. The model already knows what these tasks entail, so examples add little value.
- "Summarize this article in 3 bullet points"
- "Translate this text to Spanish"
- "Explain quantum computing to a 10-year-old"
- "Write a professional email requesting a meeting"
When Instructions Are Crystal Clear
If you can describe exactly what you want with precise, detailed instructions, zero-shot prompting is often sufficient. Clear requirements about length, format, tone, and content give the AI everything it needs without examples.
For Creative or Open-Ended Tasks
When you want the AI to be creative or generate unique content, zero-shot prompting gives it more freedom. Examples can sometimes constrain creativity by anchoring the model to specific patterns. For brainstorming, creative writing, or generating novel ideas, zero-shot often produces more diverse results.
When Speed Matters
Zero-shot prompts are faster to write and use less of the model's context window. If you're doing quick one-off tasks or need rapid results, the simplicity of zero-shot prompting is a significant advantage.
Start with zero-shot prompting. Only add examples if the initial results don't match your expectations. This saves time and keeps prompts concise.
When to Use Few-Shot Prompting
For Custom or Unusual Formats
When you need output in a specific, non-standard format, examples are invaluable. Rather than trying to describe your desired format in words, showing an example communicates your needs instantly and unambiguously.
Without examples: "Format product data as a structured text block with the name on the first line, price on the second, and features as a bulleted list."
With examples: Much clearer to show one formatted product, then ask for more in the same style.
When Consistency Is Critical
If you're processing multiple similar items and need consistent output formatting, few-shot prompting ensures uniformity. Examples establish a clear pattern that the model follows reliably across all instances.
For Domain-Specific Tasks
When working in specialized fields with specific conventions, terminology, or formats, examples help the AI understand domain-specific expectations. This is particularly valuable for technical writing, legal documents, medical content, or industry-specific analysis.
When Tone and Style Matter
Describing a writing style in words is difficult. Showing examples of the desired tone—whether it's casual, technical, humorous, or formal—helps the AI match your expectations far more accurately than verbal descriptions alone.
Instead of: "Write in a casual, friendly tone with occasional humor"
Try: "Here are two examples of our brand voice: [example 1] [example 2]. Now write about [topic] in the same style."
For Classification Tasks
When categorizing, labeling, or classifying items—especially with custom categories—few-shot prompting dramatically improves accuracy. Examples show exactly what belongs in each category and help with edge cases.
For Complex Reasoning Tasks
When you need the AI to follow specific logical steps or reasoning processes, showing examples of complete reasoning chains helps it understand not just what answer you want, but how to arrive at it.
Few-shot prompting uses more tokens (which can increase costs for API usage) and takes up more of the context window. Use it strategically when the benefits justify the overhead.
How to Craft Effective Examples
Make Examples Representative
Choose examples that represent the range of inputs you'll encounter. If you're classifying customer support tickets, include examples of different ticket types, not just the easiest or most common cases.
Show Edge Cases
Include at least one example that demonstrates how to handle ambiguous or tricky situations. This helps the AI make better decisions when faced with similar edge cases in real inputs.
When classifying sentiment, include an example like: "The product works, I guess. Not impressed but not disappointed either." → Neutral
This shows the AI how to handle mixed or ambiguous sentiments.
Keep Examples Concise
While examples should be realistic, they don't need to be lengthy. Focus on showing the key aspects of what you want. Overly long examples waste tokens without adding clarity.
Maintain Consistent Formatting
Structure all your examples identically. If your first example has "Input:" and "Output:" labels, use them in all examples. Consistency helps the AI recognize the pattern more reliably.
Use Clear Delimiters
Separate your examples from each other and from the actual task clearly. Use consistent markers like line breaks, "Example 1:", "Example 2:", or similar formatting to make the structure obvious.
When creating examples, think about what could go wrong. If the AI might misunderstand something, create an example that addresses that specific confusion.
Balance Simple and Complex Examples
Include a mix of straightforward and more challenging examples. Simple examples establish the basic pattern, while complex ones show how to handle nuance. This combination produces the most robust results.
Common Mistakes with Few-Shot Prompting
Too Many Examples
More isn't always better. Beyond 4-5 examples, you often see diminishing returns while consuming valuable context space. Focus on quality and diversity over quantity.
Examples That Contradict Each Other
If your examples show inconsistent patterns, the AI gets confused. Review your examples to ensure they demonstrate a coherent, consistent approach to the task.
Example 1: Short, casual response
Example 2: Long, formal response with citations
Example 3: Medium length, technical jargon
These examples send mixed signals about what you actually want.
Examples That Are Too Similar
If all your examples are nearly identical, the AI doesn't learn to generalize. Include variation in your examples to show the range of acceptable responses while maintaining the core pattern you want.
Forgetting to Include the Actual Task
After providing examples, some people forget to clearly indicate the new input they want processed. Always end with a clear prompt that shows "here's the new one I need you to do now."
Using Poor Quality Examples
Your examples set the standard for output quality. If your examples contain errors, inconsistencies, or poor formatting, the AI will replicate these issues. Take time to craft high-quality examples.
Advanced Few-Shot Techniques
Chain-of-Thought Prompting
For complex reasoning tasks, include examples that show the step-by-step thinking process, not just the final answer. This technique, called chain-of-thought prompting, dramatically improves performance on logic problems, math, and multi-step reasoning.
Question: "If a store has 15 apples and sells 40% of them, how many remain?"
Reasoning: "40% of 15 = 0.40 × 15 = 6 apples sold. Starting amount minus sold = 15 - 6 = 9 apples remain."
Answer: 9 apples
Graduated Examples
Present examples in order of increasing complexity. Start with a simple, clear-cut case, then progress to more nuanced or challenging examples. This scaffolding helps the AI understand both basic patterns and how to handle complexity.
Negative Examples
Sometimes it's helpful to show what you don't want. Include an example labeled as incorrect or undesirable, explaining why it's wrong. This clarifies boundaries and helps the AI avoid common pitfalls.
Dynamic Few-Shot Selection
For repeated tasks, consider maintaining a library of examples and selecting the most relevant ones for each specific prompt. Choose examples that most closely match the current input's characteristics for maximum relevance.
Combining Zero-Shot and Few-Shot
You can use a hybrid approach: provide general instructions (zero-shot style) along with 1-2 examples to clarify specific aspects. This combines the flexibility of zero-shot with the precision of few-shot prompting.
Start with zero-shot. If results are inconsistent, add 2-3 examples. If it's still not right, examine which examples would address the specific issues you're seeing.
Testing and Iteration
The best way to master few-shot prompting is experimentation. Try the same task with zero-shot, one-shot, and few-shot approaches. Compare results. Notice which types of tasks benefit most from examples and which work fine without them. Build intuition through hands-on practice.
Keep a collection of effective example sets for tasks you perform regularly. Over time, you'll develop a personal library of proven few-shot prompts that you can reuse and adapt, saving time while maintaining quality.