What is Few-shot Prompting?
Few-shot learning is a prompting technique where you provide the AI with one or several small number of examples (typically 1-10) to demonstrate the desired pattern, format, or behavior before asking it to perform a similar task. This technique leverages the AI’s ability to recognize patterns and generalize from limited examples.Why Use Few-shot Prompting?
- Improved Accuracy: Examples help the AI understand exactly what you want
- Consistent Format: Ensures outputs follow a specific structure
- Reduced Ambiguity: Clear examples eliminate guesswork
- Better Context Understanding: Shows the AI how to handle edge cases
- Domain Adaptation: Helps AI adapt to specific domains or styles
Zero-shot vs Few-shot
Zero-shot prompting involves asking the AI to perform a task without any examples, relying solely on its pre-existing knowledge. Few-shot prompting, on the other hand, provides a few examples to guide the AI’s response. Few-shot learning is generally more effective for complex tasks where context and specific patterns are crucial.One-shot vs Few-shot
One-shot prompting provides a single example to guide the AI, the idea behind one-shot learning is to show the AI how to perform a task with just one example.All variants of few-shot prompting (zero-shot, one-shot, and few-shot) can be implemented in Latitude. The choice depends on the complexity of the task and the amount of guidance needed.
Basic Implementation in Latitude
Here’s a simple few-shot learning example for email classification:Email Classification
Advanced Implementation with Variables
Let’s create a more sophisticated example that uses Latitude’s parameters system:-
Dynamic Content: We use templates (
{{ variable }}
) to insert parameters into the prompt. -
Templating Features: We demonstrate control structures like
{{for item in items }}
for arrays. -
Runtime Examples: The
examples
array parameter allows users to pass in any number of examples when calling the prompt.
Multi-step Few-shot with Chains
Latitude’s chain feature allows you to create complex few-shot workflows:Dynamic Few-shot with Conditional Logic
Use Latitude’s conditional features to adapt examples based on context:Few-shot with Agent Collaboration
Combine few-shot learning with Latitude’s agent system for complex workflows:Best Practices for Few-shot Prompting
Example Selection
Example Selection
Choose Representative Examples:
- Cover different scenarios and edge cases
- Include both positive and negative examples
- Ensure examples match your target domain
- Use diverse input formats when applicable
- Make examples clear and unambiguous
- Include enough detail without being verbose
- Show consistent formatting patterns
- Demonstrate the reasoning process when needed
Prompt Structure
Prompt Structure
Optimal Structure:
- Task Description: Clear explanation of what you want
- Examples Section: 2-10 well-chosen examples
- Input Section: Where the new data goes
- Output Section: Where the response should go
- Use consistent separators between examples
- Clearly label input and output sections
- Include field names for structured outputs
- Use markdown formatting for readability
Variable Integration
Variable Integration
Dynamic Examples:
- Use Latitude variables to customize examples
- Implement conditional logic for context-aware examples
- Store example sets in prompt references for reusability
- Allow users to provide their own examples when needed
Performance Optimization
Performance Optimization
Token Management:
- Balance between example quantity and token efficiency
- Use the most informative examples
- Consider using shorter examples for simple tasks
- Cache common example sets using prompt references
- Use more capable models (GPT-4) for complex few-shot tasks
- Consider fine-tuning for repeated patterns
- Adjust temperature based on creativity needs
- Test with different model sizes
Advanced Techniques
Self-Improving Few-shot
Create prompts that can improve their own examples:Cross-Domain Transfer
Use few-shot learning to transfer patterns across domains:Common Pitfalls and Solutions
Avoid These Common Mistakes:
- Too Many Examples: More isn’t always better; 3-7 examples are usually optimal
- Inconsistent Formatting: Make sure all examples follow the same structure
- Biased Examples: Include diverse scenarios to avoid model bias
- Unclear Boundaries: Clearly separate examples from the actual task
Pro Tips:
- Start with 2-3 examples and add more if needed
- Test your few-shot prompts with edge cases
- Use Latitude’s version control to iterate on example sets
- Combine with other techniques like Chain-of-Thought for complex reasoning
Next Steps
Now that you understand few-shot learning, explore these related techniques:- Chain-of-Thought - Add reasoning steps to your examples
- Template-based Prompting - Structure your few-shot examples
- Role Prompting - Combine examples with specific roles
- Self-Consistency - Use multiple few-shot attempts for better results