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skills/ai/prompt-engineering/references/few-shot-patterns.md

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+# Few-Shot Learning Patterns
+
+This reference provides comprehensive frameworks for implementing effective few-shot learning strategies that maximize model performance within context window constraints.
+
+## Core Principles
+
+### Example Selection Strategy
+
+#### Semantic Similarity Selection
+- Use embedding similarity to find examples closest to target input
+- Cluster similar examples to avoid redundancy
+- Select diverse representatives from different semantic regions
+- Prioritize examples that cover key variations in problem space
+
+#### Diversity Sampling Approach
+- Ensure coverage of different input types and patterns
+- Include boundary cases and edge conditions
+- Balance simple and complex examples
+- Select examples that demonstrate different solution strategies
+
+#### Progressive Complexity Ordering
+- Start with simplest, most straightforward examples
+- Progress to increasingly complex scenarios
+- Include challenging edge cases last
+- Use this ordering to build understanding incrementally
+
+## Example Templates
+
+### Classification Tasks
+
+#### Binary Classification Template
+```
+Classify if the text expresses positive or negative sentiment.
+
+Example 1:
+Text: "I love this product! It works exactly as advertised and exceeded my expectations."
+Sentiment: Positive
+Reasoning: Contains enthusiastic language, positive adjectives, and satisfaction indicators
+
+Example 2:
+Text: "The customer service was terrible and the product broke after one day of use."
+Sentiment: Negative
+Reasoning: Contains negative adjectives, complaint language, and dissatisfaction indicators
+
+Example 3:
+Text: "It's okay, nothing special but does the basic job."
+Sentiment: Negative
+Reasoning: Contains lukewarm language, lack of enthusiasm, minimal positive elements
+
+Now classify:
+Text: {input_text}
+Sentiment:
+Reasoning:
+```
+
+#### Multi-Class Classification Template
+```
+Categorize the customer inquiry into one of: Technical Support, Billing, Sales, or General.
+
+Example 1:
+Inquiry: "My account was charged twice for the same subscription this month"
+Category: Billing
+Key indicators: "charged twice", "subscription", "account", financial terms
+
+Example 2:
+Inquiry: "The app keeps crashing when I try to upload files larger than 10MB"
+Category: Technical Support
+Key indicators: "crashing", "upload files", "technical issue", "error report"
+
+Example 3:
+Inquiry: "What are your pricing plans for enterprise customers?"
+Category: Sales
+Key indicators: "pricing plans", "enterprise", business inquiry, sales question
+
+Now categorize:
+Inquiry: {inquiry_text}
+Category:
+Key indicators:
+```
+
+### Transformation Tasks
+
+#### Text Transformation Template
+```
+Convert formal business text into casual, friendly language.
+
+Example 1:
+Formal: "We regret to inform you that your request cannot be processed at this time due to insufficient documentation."
+Casual: "Sorry, but we can't process your request right now because some documents are missing."
+
+Example 2:
+Formal: "The aforementioned individual has demonstrated exceptional proficiency in the designated responsibilities."
+Casual: "They've done a great job with their tasks and really know what they're doing."
+
+Example 3:
+Formal: "Please be advised that the scheduled meeting has been postponed pending further notice."
+Casual: "Hey, just letting you know that we've put off the meeting for now and will let you know when it's rescheduled."
+
+Now convert:
+Formal: {formal_text}
+Casual:
+```
+
+#### Data Extraction Template
+```
+Extract key information from the job posting into structured format.
+
+Example 1:
+Job Posting: "We are seeking a Senior Software Engineer with 5+ years of experience in Python and cloud technologies. This is a remote position offering $120k-$150k salary plus equity."
+
+Extracted:
+- Position: Senior Software Engineer
+- Experience Required: 5+ years
+- Skills: Python, cloud technologies
+- Location: Remote
+- Salary: $120k-$150k plus equity
+
+Example 2:
+Job Posting: "Marketing Manager needed for growing startup. Must have 3 years experience in digital marketing, social media management, and content creation. San Francisco office, competitive compensation."
+
+Extracted:
+- Position: Marketing Manager
+- Experience Required: 3 years
+- Skills: Digital marketing, social media management, content creation
+- Location: San Francisco
+- Salary: Competitive compensation
+
+Now extract:
+Job Posting: {job_posting_text}
+Extracted:
+```
+
+### Generation Tasks
+
+#### Creative Writing Template
+```
+Generate compelling product descriptions following the shown patterns.
+
+Example 1:
+Product: Wireless headphones with noise cancellation
+Description: "Immerse yourself in crystal-clear audio with our premium wireless headphones. Advanced noise cancellation technology blocks out distractions while 30-hour battery life keeps you connected all day long."
+
+Example 2:
+Product: Smart home security camera
+Description: "Protect what matters most with intelligent monitoring that alerts you to activity instantly. AI-powered detection distinguishes between people, pets, and vehicles for truly smart security."
+
+Example 3:
+Product: Portable espresso maker
+Description: "Barista-quality espresso anywhere, anytime. Compact design meets professional-grade extraction in this revolutionary portable machine that delivers perfect shots in under 30 seconds."
+
+Now generate:
+Product: {product_description}
+Description:
+```
+
+### Error Correction Patterns
+
+#### Error Detection and Correction Template
+```
+Identify and correct errors in the given text.
+
+Example 1:
+Text with errors: "Their going to the park to play there new game with they're friends."
+Correction: "They're going to the park to play their new game with their friends."
+Errors fixed: "Their → They're", "there → their", "they're → their"
+
+Example 2:
+Text with errors: "The company's new policy effects every employee and there morale."
+Correction: "The company's new policy affects every employee and their morale."
+Errors fixed: "effects → affects", "there → their"
+
+Example 3:
+Text with errors: "Its important to review you're work carefully before submiting."
+Correction: "It's important to review your work carefully before submitting."
+Errors fixed: "Its → It's", "you're → your", "submiting → submitting"
+
+Now correct:
+Text with errors: {text_with_errors}
+Correction:
+Errors fixed:
+```
+
+## Advanced Strategies
+
+### Dynamic Example Selection
+
+#### Context-Aware Selection
+```python
+def select_examples(input_text, example_database, max_examples=3):
+    """
+    Select most relevant examples based on semantic similarity and diversity.
+    """
+    # 1. Calculate similarity scores
+    similarities = calculate_similarity(input_text, example_database)
+
+    # 2. Sort by similarity
+    sorted_examples = sort_by_similarity(similarities)
+
+    # 3. Apply diversity sampling
+    diverse_examples = diversity_sampling(sorted_examples, max_examples)
+
+    # 4. Order by complexity
+    final_examples = order_by_complexity(diverse_examples)
+
+    return final_examples
+```
+
+#### Adaptive Example Count
+```python
+def determine_example_count(input_complexity, context_limit):
+    """
+    Adjust example count based on input complexity and available context.
+    """
+    base_count = 3
+
+    # Complex inputs benefit from more examples
+    if input_complexity > 0.8:
+        return min(base_count + 2, context_limit)
+    elif input_complexity > 0.5:
+        return base_count + 1
+    else:
+        return max(base_count - 1, 2)
+```
+
+### Quality Metrics for Examples
+
+#### Example Effectiveness Scoring
+```python
+def score_example_effectiveness(example, test_cases):
+    """
+    Score how effectively an example teaches the desired pattern.
+    """
+    metrics = {
+        'coverage': measure_pattern_coverage(example),
+        'clarity': measure_instructional_clarity(example),
+        'uniqueness': measure_uniqueness_from_other_examples(example),
+        'difficulty': measure_appropriateness_difficulty(example)
+    }
+
+    return weighted_average(metrics, weights=[0.3, 0.3, 0.2, 0.2])
+```
+
+## Best Practices
+
+### Example Quality Guidelines
+- **Clarity**: Examples should clearly demonstrate the desired pattern
+- **Accuracy**: Input-output pairs must be correct and consistent
+- **Relevance**: Examples should be representative of target task
+- **Diversity**: Include variation in input types and complexity levels
+- **Completeness**: Cover edge cases and boundary conditions
+
+### Context Management
+- **Token Efficiency**: Optimize example length while maintaining clarity
+- **Progressive Disclosure**: Start simple, increase complexity gradually
+- **Redundancy Elimination**: Remove overlapping or duplicate examples
+- **Compression**: Use concise representations where possible
+
+### Common Pitfalls to Avoid
+- **Overfitting**: Don't include too many examples from same pattern
+- **Under-representation**: Ensure coverage of important variations
+- **Ambiguity**: Examples should have clear, unambiguous solutions
+- **Context Overflow**: Balance example count with window limitations
+- **Poor Ordering**: Place examples in logical progression order
+
+## Integration with Other Patterns
+
+Few-shot learning combines effectively with:
+- **Chain-of-Thought**: Add reasoning steps to examples
+- **Template Systems**: Use few-shot within structured templates
+- **Prompt Optimization**: Test different example selections
+- **System Prompts**: Establish few-shot learning expectations in system prompts
+
+This framework provides the foundation for implementing effective few-shot learning across diverse tasks and model types.