Leverage examples to dramatically improve AI accuracy and consistency
One of the most powerful ways to improve AI performance is by showing it examples of what you want. Few-shot prompting provides the model with demonstrations that establish patterns, tone, and format — leading to far more accurate and consistent results than asking without context.
Asking the AI to perform a task with no examples — just instructions. The model relies entirely on its training data to understand what you want.
Example Zero-Shot Prompt:
"Classify the sentiment of this customer review as positive, negative, or neutral: 'The product arrived quickly but the quality was disappointing.'"
No examples given — the model must infer what "sentiment classification" means based on its training.
When Zero-Shot Works Well:
Providing one example to demonstrate the pattern, format, or style you want.
Example One-Shot Prompt:
"Classify the sentiment of customer reviews as positive, negative, or neutral."
Example:
Review: "Absolutely love this product! Best purchase I've made."
Sentiment: Positive
Review: "The product arrived quickly but the quality was disappointing."
Sentiment: ?
The single example helps establish the format and expectations.
Providing multiple examples (typically 2-5) to establish a clear pattern and train the model's in-context learning.
Example Few-Shot Prompt:
"Classify the sentiment of customer reviews as positive, negative, or neutral."
Review: "Absolutely love this product! Best purchase I've made."
Sentiment: Positive
Review: "Terrible quality. Broke after one use. Do not recommend."
Sentiment: Negative
Review: "It's okay. Does what it's supposed to do, nothing special."
Sentiment: Neutral
Review: "The product arrived quickly but the quality was disappointing."
Sentiment: ?
When Few-Shot Works Best:
Research shows that few-shot prompting can improve accuracy by 20-40% compared to zero-shot for complex tasks.
Zero-Shot
60-70%
Average accuracy on complex classification
One-Shot
75-85%
Improved pattern recognition
Few-Shot
85-95%
Near-expert level performance
The optimal number of examples depends on task complexity, but research suggests diminishing returns beyond a certain point:
2-3 Examples
Good for simple tasks with clear patterns (sentiment analysis, basic classification)
3-5 Examples
Optimal sweet spot for most tasks — balances performance gains with token efficiency
5-10 Examples
For complex tasks with many edge cases or nuanced requirements
10+ Examples
Usually unnecessary and inefficient — consider fine-tuning instead if you need this many
Pro Tip: Start with 3 diverse examples covering different scenarios, then add more only if performance doesn't meet requirements. More examples = higher token costs.
Choose examples that cover different variations of the task to help the model generalize better.
Example: Email Tone Classification
Use identical structure across all examples so the model learns the exact format you want.
Good Format Consistency:
Input: [text]
Category: [category]
Confidence: [high/medium/low]
Input: [text]
Category: [category]
Confidence: [high/medium/low]
Ensure examples are representative of the actual data the model will process in production.
❌ Poor Examples:
Simple, textbook cases that don't reflect real-world complexity
✓ Good Examples:
Real or realistic data with typical messiness and edge cases
Test if zero-shot works first — don't overcomplicate if it's not needed
3 excellent, diverse examples beat 10 similar ones
Use line breaks, separators, or numbering to distinguish examples
Each example consumes tokens — balance performance with cost
Some models weight recent examples more heavily — experiment with order
When the model makes mistakes, add examples covering those scenarios
Implement few-shot prompting strategies to achieve production-grade accuracy