Few-Shot Prompt Builder
Create few-shot learning prompts with example input-output pairs
Task Description
Example Pairs (1)
Output Format
Generated Prompt
[
{
"role": "system",
"content": "You are a helpful assistant."
}
] Few-Shot Tips
- 3-5 examples is usually optimal for most tasks
- Include diverse examples that cover edge cases
- Keep examples consistent in format and length
- Put your best, clearest examples first
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Complete Guide to Few-Shot Learning Prompts
How to Use This Tool
The Few-Shot Prompt Builder helps you create effective few-shot learning prompts by structuring example input-output pairs. Follow these steps:
Describe Your Task
Enter a clear description of the task you want the AI to perform. This helps the model understand the context and expected behavior. For example: "Classify customer reviews as positive or negative."
Add Example Pairs
Create input-output example pairs that demonstrate the desired behavior. Each example should show an input and the corresponding expected output. Click "Add Example" to create more pairs.
Choose Output Format
Select the format for your prompt: Chat Messages (JSON array for chat APIs), Completion (text-based format), or XML Tags (structured markup for Claude). Each format has different use cases.
Copy or Download
Review the generated prompt in the output panel. Copy it to your clipboard or download it as a file. The token estimate helps you understand the cost impact.
What is Few-Shot Learning?
Few-shot learning is a prompting technique where you provide the AI model with a small number of examples (typically 2-10) to demonstrate the desired behavior before asking it to perform the task on new inputs. This approach is remarkably effective because it:
- • Improves accuracy: Examples help the model understand exactly what format and style of output you expect.
- • Reduces ambiguity: Showing is often clearer than telling when it comes to AI instructions.
- • Handles format consistency: Examples establish the output format implicitly.
- • Enables complex tasks: Some tasks that are hard to describe become easy to demonstrate.
How Many Examples Do You Need?
| Task Complexity | Recommended Examples | Example Tasks |
|---|---|---|
| Simple | 1-2 | Yes/no classification, simple extraction |
| Moderate | 3-5 | Sentiment analysis, categorization, formatting |
| Complex | 5-10 | Multi-step reasoning, specialized domains |
| Nuanced | 10+ | Edge cases, subtle distinctions, style matching |
Output Format Comparison
Chat Messages
JSON array format compatible with OpenAI and similar chat APIs. Best for conversational AI applications.
- OpenAI Chat API
- Claude Messages API
- Multi-turn conversations
Completion
Plain text format with labeled examples. Works with any text completion model or simple prompts.
- Legacy Completion APIs
- Simple integrations
- Human readable
XML Tags
Structured XML format that Claude models particularly excel at parsing and following.
- Claude models
- Structured extraction
- Clear boundaries
Best Practices for Examples
Creating Effective Examples
- Be diverse: Include examples that cover different scenarios and edge cases.
- Stay consistent: Use the same format and style across all examples.
- Start with the clearest: Put your best, most unambiguous examples first.
- Match real inputs: Examples should resemble the actual inputs you'll process.
- Include edge cases: Show how to handle tricky or boundary situations.
- Keep it concise: Long examples consume tokens without adding value.
Common Few-Shot Use Cases
Classification
Categorize text into predefined classes (sentiment, topic, intent).
Extraction
Pull specific information from unstructured text (names, dates, entities).
Transformation
Convert text from one format/style to another (rewriting, translation).
Reasoning
Demonstrate step-by-step thinking for complex problem solving.
Few-Shot vs Zero-Shot vs Fine-Tuning
When to Use Each Approach
- Zero-shot: Simple, well-defined tasks that the model already understands (translation, summarization).
- Few-shot: Custom tasks, specific formats, domain-specific behavior, or when examples clarify ambiguity.
- Fine-tuning: Very high volume (1000s of requests/day), consistent specialized behavior, or latency-sensitive applications.
Token Efficiency
Few-shot examples consume tokens with every request. To optimize costs:
- • Use the minimum number of examples needed for consistent results.
- • Keep examples as short as possible while remaining clear.
- • Consider caching or system prompts for frequently used examples.
- • Test with 2-3 examples first, then add more only if accuracy improves.