Few-shot learning represents one of the most practical and powerful capabilities of modern language models. By providing just a few well-chosen examples, you can guide AI models to understand new tasks. However, in 2026, few-shot effectiveness depends significantly on your model type.
Understanding Few-Shot Learning
Few-shot learning allows models to generalize from a small number of examples (typically 1-10) provided within the prompt itself. This approach leverages the model's pre-trained knowledge and pattern recognition capabilities.
The Learning Spectrum
- Zero-shot: No examples, just instructions
- One-shot: Single example
- Few-shot: 2-10 examples
- Many-shot: 10+ examples (now practical with 1M+ token context windows)
Example Structure and Format
Basic Few-Shot Template
Task description: [Brief explanation]
Example 1:
Input: [Example input]
Output: [Desired output]
Example 2:
Input: [Example input]
Output: [Desired output]
Now solve:
Input: [Your actual input]
Output:
Real-World Example: Email Classification
Classify emails as "urgent", "normal", or "spam":
Example 1:
Input: "CONGRATULATIONS! You've won $1,000,000! Click here now!"
Output: spam
Example 2:
Input: "Hi Sarah, can you send the quarterly report by EOD? Thanks!"
Output: urgent
Example 3:
Input: "Newsletter: 10 Tips for Better Productivity"
Output: normal
Now classify:
Input: "URGENT: Server down, need immediate attention!"
Output:
Selecting Effective Examples
1. Diversity is Key
Choose examples that cover different aspects of the task:
- Different input types: Vary length, style, complexity
- Different output categories: Cover all possible outcomes
- Edge cases: Include borderline or tricky examples
2. Quality over Quantity
Bad: 10 similar examples
Good: 3-5 diverse, high-quality examples
3. Representative Examples
Examples should reflect the distribution of real-world inputs you expect.
Advanced Few-Shot Strategies
Gradient-Based Example Selection
Order examples from simple to complex:
Example 1: [Simple, clear case]
Example 2: [Moderate complexity]
Example 3: [Complex or edge case]
Chain-of-Thought Few-Shot
Combine CoT reasoning with few-shot learning (especially effective on instruction models):
Example 1:
Input: "What's 15% of 240?"
Reasoning: To find 15% of 240, I multiply 240 by 0.15. 240 × 0.15 = 36.
Output: 36
Example 2:
Input: "What's 25% of 80?"
Reasoning: To find 25% of 80, I multiply 80 by 0.25. 80 × 0.25 = 20.
Output: 20
Domain-Specific Applications
Code Generation
Generate Python functions based on descriptions:
Example 1:
Description: Calculate the area of a circle
Code:
import math
def circle_area(radius):
return math.pi * radius ** 2
Example 2:
Description: Check if a number is prime
Code:
def is_prime(n):
if n < 2:
return False
for i in range(2, int(n ** 0.5) + 1):
if n % i == 0:
return False
return True
Data Extraction
Extract structured information from text:
Example 1:
Text: "John Smith, age 35, works as a Software Engineer at TechCorp."
Extracted: {"name": "John Smith", "age": 35, "job": "Software Engineer", "company": "TechCorp"}
Example 2:
Text: "Dr. Sarah Johnson, 42, is a cardiologist at City Hospital."
Extracted: {"name": "Dr. Sarah Johnson", "age": 42, "job": "cardiologist", "company": "City Hospital"}
Optimization Techniques
1. Example Ordering
Test different orderings to find what works best:
- Chronological
- Difficulty-based
- Category-based
- Random
2. Format Consistency
Maintain consistent formatting across examples:
Input: [Always same format]
Output: [Always same format]
3. Delimiter Usage
Use clear delimiters to separate examples:
Example 1:
---
Input: ...
Output: ...
---
Example 2:
---
Input: ...
Output: ...
---
Common Pitfalls and Solutions
1. Biased Examples
Problem: All examples lean toward one category Solution: Ensure balanced representation
2. Overly Complex Examples
Problem: Examples are too sophisticated for the task Solution: Start simple, add complexity gradually
3. Inconsistent Formatting
Problem: Examples use different formats Solution: Standardize input/output structure
4. Insufficient Context
Problem: Examples don't provide enough information Solution: Include relevant context and reasoning
Measuring Few-Shot Performance
Evaluation Metrics
- Accuracy: Percentage of correct outputs
- Consistency: Similar inputs produce similar outputs
- Generalization: Performance on unseen examples
- Efficiency: Performance relative to example count
A/B Testing
Compare different example sets:
- Different number of examples (2 vs 5 vs 8)
- Different example types
- Different ordering strategies
Few-Shot in 2026: When to Use It (and When Not To)
A critical insight for 2026: few-shot learning effectiveness depends on your model type.
On Instruction Models (GPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash/Pro)
Few-shot learning dramatically improves performance. Use it liberally:
- Provides clear task examples
- Reduces hallucination
- Improves consistency
- Works well with chain-of-thought
Few-shot examples are still a cornerstone technique for instruction models.
On Reasoning Models (o1, o3, o3-mini)
Few-shot examples can actually hurt performance. These models:
- Prefer to reason from first principles
- Have built-in reasoning phases that work better with problem clarity than example imitation
- May get confused or distracted by examples
- Perform best with a clear problem statement and minimal scaffolding
For o1/o3 models, use zero-shot instead:
Good: "Solve this problem: [problem statement]"
Bad: "Here are 5 examples of solved problems. Now solve: [problem statement]"
Hybrid Approach
If you're unsure which model you're using or need to support both:
- Try zero-shot first (works on both)
- If results are weak on instruction models, add few-shot examples
- Monitor whether few-shot helps or hurts each model type
Many-Shot Prompting: A New Frontier in 2026
With 1M+ token context windows now standard on Gemini 2.0 and Claude supporting 200k tokens, many-shot prompting is a new technique:
Instead of 3-5 examples, you can now provide dozens or even hundreds of examples:
Task: Classify customer feedback sentiment
Example 1: "This product is amazing!" → positive
Example 2: "Terrible experience, would not recommend" → negative
Example 3: "It's okay, nothing special" → neutral
Example 4: "Best purchase I've made!" → positive
... [40+ more examples]
Now classify:
Input: "The quality is decent but overpriced"
Output:
With massive context windows, many-shot can approach fine-tuning performance without retraining models. This is especially effective for instruction models.
Few-shot learning bridges the gap between general AI capabilities and specific task requirements, making it an essential technique for practical AI applications in 2026. The key is matching your few-shot strategy to your model's type.
Keep Exploring
Put these techniques to work with more resources from our library:
- Chain-of-Thought Prompting: The Secret to Better ChatGPT Answers (2026 Guide) — Combine CoT with few-shot for maximum accuracy; learn how reasoning models differ.
- The Evolution of Prompt Engineering in 2026: From Basic Queries to Agentic AI — A comprehensive overview of all prompting techniques including agent design.
- How Large Language Models Work in 2026: A Practical Guide for Prompt Engineers — Understand why few-shot works differently on different models.
- Browse our prompt library — 60+ ready-to-use templates for image, video, and UX design.