> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.latitude.so/llms.txt
> Use this file to discover all available pages before exploring further.

# Self-Consistency

> Learn how to implement self-consistency to improve AI reasoning reliability through multiple sampling and majority voting

## What is Self-Consistency?

Self-consistency is a prompting technique that improves the reliability of AI reasoning by generating multiple responses to the same question and then selecting the most consistent answer through majority voting. Unlike traditional Chain-of-Thought prompting that uses greedy decoding for a single reasoning path, self-consistency leverages diverse sampling to explore multiple reasoning perspectives before converging on the most reliable answer.

## Why Use Self-Consistency?

* **Improved Accuracy**: Multiple samples reduce the impact of random errors and greedy decoding limitations
* **Better Reasoning**: Helps identify the most logical solution path from diverse perspectives
* **Reduced Hallucinations**: Inconsistent responses are filtered out through majority voting
* **Confidence Assessment**: Provides pseudo-probability likelihood of answer correctness
* **Complex Problem Solving**: Particularly effective for math, logic, and multi-step reasoning where single attempts may fail
* **Robust Decision Making**: Overcomes limitations of single reasoning paths in ambiguous scenarios

## Basic Implementation in Latitude

Here's a simple self-consistency example for classification tasks:

```markdown Classification with Self-Consistency theme={null}
---
provider: OpenAI
model: gpt-4o
temperature: 0.7
---

# Content Classification

Classify the following content and explain your reasoning step by step.

## Content:
{{ content_to_classify }}

## Classification Process:
Let me analyze this step by step:

1. **Content Analysis:**
   - What type of content is this?
   - What are the key indicators?

2. **Context Evaluation:**
   - What contextual clues are present?
   - How do tone and language affect classification?

3. **Risk Assessment:**
   - What potential impacts should be considered?
   - Are there any warning signs?

4. **Final Classification:**
   Based on my analysis: [CATEGORY]

**Reasoning:** [Detailed explanation of decision]
```

## How Self-Consistency Works

The self-consistency process follows three key steps:

1. **Diverse Path Generation**: The same prompt is submitted multiple times with higher temperature settings (0.6-0.8) to encourage different reasoning approaches and perspectives
2. **Answer Extraction**: Each response is analyzed to extract the core answer or classification, regardless of the reasoning path taken
3. **Majority Voting**: The most frequently occurring answer across all samples is selected as the final result

This approach provides a form of confidence scoring - answers that appear consistently across multiple reasoning paths are more likely to be correct than those that appear only once.

## Advanced Implementation with Multiple Samples

Let's create a more sophisticated example that uses Latitude's chain feature to generate and compare multiple reasoning paths:

<CodeGroup>
  ```markdown Advanced Self-Consistency theme={null}
  ---
  provider: OpenAI
  model: gpt-4o
  temperature: 0.8
  ---

  <step>
  # Reasoning Sample 1

  Solve this problem using your preferred approach:

  ## Problem:
  {{ reasoning_problem }}

  ## Solution Path 1:
  Think through this step by step and provide your final answer.
  </step>

  <step>
  # Reasoning Sample 2

  Solve the same problem using a different approach if possible:

  ## Problem:
  {{ reasoning_problem }}

  ## Solution Path 2:
  Think through this step by step and provide your final answer.
  </step>

  <step>
  # Reasoning Sample 3

  Solve the problem one more time, focusing on accuracy:

  ## Problem:
  {{ reasoning_problem }}

  ## Solution Path 3:
  Think through this step by step and provide your final answer.
  </step>

  <step>
  # Self-Consistency Analysis

  Review the three solution paths above and determine the most consistent answer:

  ## Analysis:
  1. **Compare the final answers:** Are they the same or different?
  2. **Evaluate reasoning quality:** Which path has the most sound logic?
  3. **Identify consensus:** What answer appears most frequently?

  ## Final Consistent Answer:
  Based on the analysis above, the most reliable answer is:

  **Answer:**
  **Confidence Level:**
  **Reasoning:**
  </step>
  ```
</CodeGroup>

In this advanced example:

1. **Multiple Sampling**: We generate three independent solutions with higher temperature for diversity
2. **Chain Processing**: Each step builds on the previous ones for comparison
3. **Consistency Analysis**: A final step evaluates and selects the best answer
4. **Confidence Assessment**: The system provides a confidence level based on agreement

## Logic and Reasoning Self-Consistency

Use self-consistency for complex logical problems:

<CodeGroup>
  ```markdown Logic Problem Self-Consistency theme={null}
  ---
  provider: OpenAI
  model: gpt-4o
  temperature: 0.6
  ---

  <step>
  # Deductive Reasoning Approach

  Solve this logic problem using deductive reasoning:

  ## Problem:
  {{ logic_problem }}

  ## Deductive Solution:
  Start with the given facts and work logically to the conclusion:

  1. **Given facts:**
  2. **Logical deductions:**
  3. **Conclusion:**
  </step>

  <step>
  # Inductive Reasoning Approach

  Solve the same problem using inductive reasoning:

  ## Problem:
  {{ logic_problem }}

  ## Inductive Solution:
  Look for patterns and make generalizations:

  1. **Observe patterns:**
  2. **Form hypothesis:**
  3. **Test and conclude:**
  </step>

  <step>
  # Abductive Reasoning Approach

  Solve using abductive reasoning (inference to best explanation):

  ## Problem:
  {{ logic_problem }}

  ## Abductive Solution:
  Find the most likely explanation:

  1. **Observations:**
  2. **Possible explanations:**
  3. **Best explanation:**
  </step>

  # Logic Consensus

  Compare all three reasoning approaches

  ## Consensus Analysis:
  - **Agreement level:** Do all approaches reach the same conclusion?
  - **Strongest reasoning:** Which approach provides the most convincing logic?
  - **Consistency score:** How well do the results align?

  ## Final Answer:
  ```
</CodeGroup>

## Multi-Agent Self-Consistency

Combine self-consistency with Latitude's agent system for specialized reasoning:

<CodeGroup>
  ```markdown Multi-Agent Self-Consistency theme={null}
  ---
  provider: OpenAI
  model: gpt-4o
  temperature: 0.5
  type: agent
  agents:
    - agents/mathematician
    - agents/logician
    - agents/analyst
  ---

  # Multi-Expert Self-Consistency

  Get multiple expert opinions and find the consensus:

  ## Problem:
  {{ complex_problem }}

  ## Expert Consultation:
  1. **Mathematician**: Analyze from a mathematical perspective
  2. **Logician**: Apply formal logical reasoning
  3. **Analyst**: Use analytical problem-solving methods

  Coordinate with all experts and provide a self-consistent final answer.

  ```

  ```markdown agents/mathematician theme={null}
  ---
  provider: OpenAI
  model: gpt-4o
  temperature: 0.4
  type: agent
  ---

  # Mathematics Expert

  I am a mathematics expert specializing in problem-solving with rigorous mathematical methods.

  ## Problem Analysis:
  {{ complex_problem }}

  ## Mathematical Approach:
  1. **Identify mathematical concepts involved**
  2. **Apply relevant formulas and theorems**
  3. **Show detailed calculations**
  4. **Verify results through alternative methods**

  ## Mathematical Solution:
  ```

  ```markdown agents/logician theme={null}
  ---
  provider: OpenAI
  model: gpt-4o
  temperature: 0.4
  type: agent
  ---

  # Logic Expert

  I am a logic expert specializing in formal reasoning and logical analysis.

  ## Problem Analysis:
  {{ complex_problem }}

  ## Logical Approach:
  1. **Structure the problem logically**
  2. **Identify premises and conclusions**
  3. **Apply logical rules and principles**
  4. **Check for logical consistency**

  ## Logical Solution:
  ```

  ```markdown agents/analyst theme={null}
  ---
  provider: OpenAI
  model: gpt-4o
  temperature: 0.4
  type: agent
  ---

  # General Analyst

  I am a general analyst specializing in systematic problem-solving and critical thinking.

  ## Problem Analysis:
  {{ complex_problem }}

  ## Analytical Approach:
  1. **Break down the problem systematically**
  2. **Consider multiple perspectives**
  3. **Evaluate evidence and assumptions**
  4. **Synthesize findings**

  ## Analytical Solution:
  ```
</CodeGroup>

## Best Practices for Self-Consistency

<AccordionGroup>
  <Accordion title="Sample Generation">
    **Optimal Sampling**:

    * Use 3-5 samples for most problems (balance cost vs. accuracy)
    * Increase temperature (0.6-0.8) to encourage diverse reasoning paths and overcome greedy decoding
    * Ensure each sample approaches the problem independently
    * Vary the prompt slightly to encourage different analytical perspectives

    **Quality Control**:

    * Generate enough samples to identify patterns
    * Filter out obviously flawed reasoning
    * Weight samples based on reasoning quality, not just frequency
    * Consider partial agreements in complex problems
  </Accordion>

  <Accordion title="Consistency Analysis">
    **Evaluation Criteria**:

    * **Answer consistency**: Do multiple samples reach the same conclusion?
    * **Reasoning quality**: Which reasoning paths are most sound?
    * **Method diversity**: Are different valid approaches represented?
    * **Confidence indicators**: How certain can we be about the consensus?

    **Analysis Techniques**:

    * Majority voting for clear disagreements
    * Weighted voting based on reasoning quality
    * Partial credit for answers that are close but not identical
    * Meta-reasoning about why inconsistencies occur
  </Accordion>

  <Accordion title="Problem Selection">
    **Best Use Cases**:

    * Classification tasks with potential ambiguity
    * Mathematical word problems
    * Logical reasoning puzzles
    * Multi-step analytical tasks
    * Questions with clear right/wrong answers where reasoning path matters
    * Security-sensitive decisions requiring high confidence

    **Less Suitable Cases**:

    * Creative writing tasks
    * Subjective opinion questions
    * Simple factual lookups
    * Tasks requiring consistent style/voice
  </Accordion>

  <Accordion title="Performance Optimization">
    **Efficiency Tips**:

    * Use parallel processing when possible
    * Cache common problem types
    * Implement early stopping if consensus is clear
    * Balance sample count with accuracy needs

    **Cost Management**:

    * Start with fewer samples and increase if needed based on consistency scores
    * Use cheaper models for initial sampling, better models for final analysis
    * Implement confidence thresholds to determine optimal sample count
    * Consider the cost trade-off: higher accuracy vs. increased computational expense
    * Remember that self-consistency has high costs but provides pseudo-probability confidence
  </Accordion>
</AccordionGroup>

## Advanced Techniques

### Adaptive Self-Consistency

Create prompts that adjust based on initial consistency. You can [play with it here](https://app.latitude.so/share/d/2a66d7c5-841d-4217-81cb-f97610ac9374)

<CodeGroup>
  ```markdown Adaptive Self-Consistency theme={null}
  ---
  provider: OpenAI
  model: gpt-4.1-mini
  temperature: 0.7
  ---

  <step>
  # Initial Sample Generation

  Generate 3 initial solutions:

  ## Problem: {{ problem }}

  ### Solution 1:
  ### Solution 2:
  ### Solution 3:
  </step>
  <step as="consistency_check" schema={{{type: "object", properties: {additional_samples: {type: "boolean"}}, required: ["additional_samples"]}}}>
  # Check Initial Consistency

    Evaluate the consistency of initial samples

  ## Consistency Analysis:
    - Are the answers consistent? (Yes/No)
    - Confidence level in consensus: (1-10)
    - Need for additional samples: (Yes/No)

  ## Decision:
    If consistency is low (< 7/10), recommend generating 2-3 additional samples.
    If consistency is high (≥ 7/10), proceed with current consensus.
  </step>

  {{ if consistency_check.additional_samples }}
    <step>
      Generate 2 more solutions using different approaches:

      ## Problem: {{ problem }}

      ### Solution 4:
      ### Solution 5:

    </step>
  {{ endif }}

  # Final Consensus
  Based on all available samples, determine the final answer:

  ## Final Self-Consistent Answer:
  ```
</CodeGroup>

<Note>Note how we used [structured outputs](/guides/prompt-manager/json-output) to capture the consistency check results and decide whether to generate additional samples.</Note>

### Self-Consistency with Uncertainty Quantification

Implement self-consistency that quantifies uncertainty:

<CodeGroup>
  ```markdown Uncertainty-Aware Self-Consistency theme={null}
  ---
  provider: OpenAI
  model: gpt-4o
  temperature: 0.8
  ---

  <step>
  # Generate Diverse Solutions

  Create 5 solutions with different reasoning strategies:

  ## Problem: {{ problem }}

  ### Strategy 1 - Direct Approach:
  ### Strategy 2 - Step-by-step Breakdown:
  ### Strategy 3 - Alternative Method:
  ### Strategy 4 - Verification Focus:
  ### Strategy 5 - Edge Case Consideration:
  </step>

  <step>
  # Uncertainty Quantification

  Analyze the uncertainty in our solutions

  ## Uncertainty Assessment:
  1. **Answer Distribution**: What answers appeared and how often?
  2. **Reasoning Confidence**: How confident was each reasoning path?
  3. **Method Agreement**: Do different methods agree?
  4. **Edge Case Handling**: How well are corner cases addressed?

  ## Uncertainty Metrics:
  - **Consensus Strength**: (0-100%)
  - **Reasoning Diversity**: (Low/Medium/High)
  - **Confidence Interval**: (if applicable)
  - **Uncertainty Sources**: (List main sources of disagreement)

  ## Final Answer with Uncertainty:
  **Most Likely Answer:**
  **Confidence Level:**
  **Alternative Possibilities:**
  **Key Uncertainties:**
  </step>
  ```
</CodeGroup>

## Integration with Other Techniques

Self-consistency works well combined with other prompting techniques:

* **Chain-of-Thought + Self-Consistency**: Generate multiple detailed reasoning chains to overcome greedy decoding limitations
* **Few-Shot + Self-Consistency**: Use examples to guide consistent reasoning patterns across multiple samples
* **Role-Playing + Self-Consistency**: Have different expert personas solve the same problem independently
* **Iterative Refinement + Self-Consistency**: Use consensus to improve solution quality through multiple rounds

The key is to maintain the core principle: generate multiple independent solutions and use agreement as a signal of reliability, while addressing the inherent limitations of single-path reasoning.

## Related Techniques

Explore these complementary prompting techniques to enhance your AI applications:

* **[Chain-of-Thought](./chain-of-thought)** - Break down complex problems into step-by-step reasoning
* **[Tree-of-Thoughts](./tree-of-thoughts)** - Explore multiple reasoning paths systematically
* **[Few-Shot Learning](./few-shot-learning)** - Use examples to guide AI behavior and improve consistency