The Science Behind Few-Shot Learning#

Few-shot learning leverages the model’s in-context learning capabilities .

This is essentially example-based programming—instead of writing explicit rules, we demonstrate the desired behavior through carefully crafted examples .

When to Use Few-Shot Learning#

• Fixed-format tasks: JSON, XML generation
• Style mimicry: Writing in specific tones or formats
• Complex rule following: Tasks with intricate, hard-to-verbalize requirements
• Domain-specific outputs: Industry jargon or specialized formats

Practical Example: Sentiment Analysis with Structured Output#

Let’s see few-shot learning in action for a sentiment analysis task that requires specific JSON formatting:

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**Input**: "The product is great, but shipping was too slow."
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**Output**: `{"sentiment": "mixed", "product": "positive", "logistics": "negative"}`
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**Input**: "This was a perfect shopping experience."
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**Output**: `{"sentiment": "positive", "product": "positive", "logistics": "positive"}`
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**Input**: "Screen has defects, customer service won't help."
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**Output**: `{"sentiment": "negative", "product": "negative", "customer_service": "negative"}`
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**New Input**: "Phone battery life is excellent, but packaging was damaged."
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**Model Output**: `{"sentiment": "mixed", "product": "positive", "packaging": "negative"}`

Notice how the model learned to:

1. Use the “mixed” sentiment category for conflicting aspects
2. Break down feedback into specific components
3. Apply consistent JSON formatting
4. Infer new categories (“packaging”) when needed

Best Practices for Few-Shot Prompting#

Diversity is Key: Include examples that cover edge cases and variations .

Quality over Quantity: 3-5 well-chosen examples often outperform 10 mediocre ones.

Order Matters: Place your strongest, clearest examples first to establish the pattern.

Beyond “Step by Step”#

While simple “think step by step” prompts work for basic problems, complex mathematical or logical challenges require structured, multi-layered reasoning .

Advanced CoT Prompting Structure#

For complex problems, specify the reasoning framework explicitly:

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Q: [Complex word problem]
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Solve this systematically:
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1. **Define variables**: Clearly identify what each variable represents
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2. **List known conditions**: Extract all given information
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3. **Establish equations**: Translate conditions into mathematical relationships
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4. **Solve step-by-step**: Show each algebraic manipulation
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5. **Verify reasonableness**: Check if the answer makes logical sense

Self-Consistency: The Voting Mechanism#

Self-consistency is a decoding strategy that significantly improves reasoning accuracy . Instead of taking the first answer, the technique:

1. Generates multiple reasoning paths (due to model randomness)
2. Compares final answers across different reasoning chains
3. Selects the most frequent answer through majority voting

Research shows self-consistency can improve performance by 17.9% on GSM8K math problems and 11.0% on SVAMP reasoning tasks .

Implementation Example#

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Prompt: "A train travels 120 miles in 2 hours, then 180 miles in 3 hours. What's the average speed for the entire journey? Think step by step."
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**Reasoning Path 1**:
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Total distance = 120 + 180 = 300 miles
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Total time = 2 + 3 = 5 hours
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Average speed = 300/5 = 60 mph
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**Reasoning Path 2**:
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First segment: 120 miles ÷ 2 hours = 60 mph
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Second segment: 180 miles ÷ 3 hours = 60 mph
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Total journey: 300 miles ÷ 5 hours = 60 mph
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**Reasoning Path 3**:
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Distance₁ = 120, Time₁ = 2
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Distance₂ = 180, Time₂ = 3
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Average = (Distance₁ + Distance₂)/(Time₁ + Time₂) = 300/5 = 60 mph
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**Consensus Answer**: 60 mph (3/3 agreement)

The Internal Editor Approach#

Self-critique prompting asks the model to critically examine its own output after generation . This technique effectively catches errors that occur due to “momentary lapses” in reasoning.

Core Self-Critique Patterns#

Basic Verification:

• “Check your work.”
• “Review the above response and identify any potential errors.”
• “Is this answer consistent with the facts provided?”

Structured Self-Review:

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After providing your answer, please:
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1. **Accuracy Check**: Verify all calculations and facts
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2. **Logic Review**: Ensure reasoning steps follow logically
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3. **Completeness Assessment**: Confirm all parts of the question are addressed
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4. **Alternative Perspective**: Consider if there's another valid interpretation

Self-Verification in Practice#

Self-verification uses a dual-process approach :

1. Forward Reasoning: Generate initial answer with CoT
2. Backward Verification: Use the answer to predict original conditions

Example:

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**Initial Problem**: "Jackie has 10 apples. Adam has 8 apples. How many more apples does Jackie have than Adam?"
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**Forward Answer**: "Jackie has 2 more apples than Adam."
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**Backward Verification**: "If Jackie has X apples, Adam has 8 apples, and Jackie has 2 more apples than Adam, what is X?"
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**Verification Answer**: "X = 8 + 2 = 10 apples"
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**Consistency Check**: ✓ Original condition confirmed

Code Generation with Self-Critique#

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**Round 1 Prompt**: "Write a Python function to calculate factorial."
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**Round 1 Output**:
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```python
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def factorial(n):
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    return 1 if n == 0 else n * factorial(n-1)

Round 2 Prompt: “Review this function. Are there any edge cases it doesn’t handle correctly?” Round 2 Output: “Yes, this function doesn’t handle negative numbers. It would cause infinite recursion. I should add a check for n < 0.”

Improved Version:

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def factorial(n):
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    if n < 0:
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        raise ValueError("Factorial is not defined for negative numbers")
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    return 1 if n == 0 else n * factorial(n-1)

Directed Output Formatting#

Force the model to output in specific, parseable formats for seamless integration with other systems:

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"Analyze this customer feedback and output your response as a JSON object with keys 'sentiment', 'urgency_level', 'department', and 'suggested_action'."

Tool Use Simulation#

Advanced prompting can make models understand when external tools are needed and generate structured requests:

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**Prompt**: "What is the square root of 2024 multiplied by pi? Use a calculator if needed."
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**Model Output**:
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```json
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{
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  "tool": "calculator",
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  "operation": "sqrt(2024) * pi",
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  "reasoning": "This requires precise mathematical calculation beyond mental math capabilities"
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}

The Multi-Technique Prompt#

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**Role**: You are a senior policy analyst for a technology think tank.
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**Task**: Analyze the following tech policy question using our structured approach.
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**Response Framework**:
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a) **Executive Summary** (2-3 sentences)
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b) **Key Points** (bulleted list)
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c) **Underlying Assumptions** (what premises does this analysis rest on?)
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d) **Follow-up Research Questions** (3 specific queries for deeper investigation)
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**Self-Review Process**:
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After your analysis, please:
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1. Review for potential bias or missing perspectives
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2. Verify factual claims against your knowledge
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3. Suggest specific search queries to validate recent developments
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**Question**: [Insert complex policy question here]

Why This Works#

1. Role-playing establishes expertise and perspective
2. Structured framework ensures comprehensive coverage
3. Self-review catches errors and biases
4. Tool integration (search suggestions) extends capabilities

Sequential Technique Application#

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**Step 1**: Use few-shot learning to establish format
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**Step 2**: Apply complex CoT for reasoning
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**Step 3**: Implement self-consistency for verification
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**Step 4**: Add self-critique for final review

Parallel Technique Integration#

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**Simultaneous Application**:
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- Few-shot examples within CoT demonstrations
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- Self-critique questions embedded in the reasoning process
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- Tool use suggestions integrated throughout

The Context Window Trap#

Problem: Too many examples exceed model limits Solution: Use representative examples, not exhaustive ones

The Overthinking Paradox#

Problem: Excessive self-critique leads to analysis paralysis Solution: Limit critique rounds to 1-2 iterations

The Consistency Illusion#

Problem: Self-consistency might reinforce systematic errors Solution: Combine with external validation when possible

The REFINE Cycle#

1. Run initial prompt with few-shot examples
2. Evaluate output quality and consistency
3. Fine-tune examples and instructions
4. Implement self-critique mechanisms
5. Navigate edge cases and errors
6. Enhance with additional techniques as needed

Development Tools#

• Prompt versioning: Track iterations and performance
• A/B testing: Compare technique combinations
• Output analysis: Measure consistency and accuracy

Evaluation Metrics#

• Accuracy: Correctness of final answers
• Consistency: Agreement across multiple runs
• Efficiency: Token usage vs. quality trade-offs
• Robustness: Performance on edge cases

The Responsibility Factor#

With great power comes great responsibility. These techniques can also generate:

• More sophisticated misinformation
• Harder-to-detect reasoning errors
• Complex biases embedded in multi-step processes

Always apply ethical guidelines and validation procedures when deploying advanced techniques in production systems.