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:

Classification with Self-Consistency

---
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:

Advanced Self-Consistency

---
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>

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:

Logic Problem Self-Consistency

---
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:

Multi-Agent Self-Consistency

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

Multi-Agent Self-Consistency

---
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.

Best Practices for Self-Consistency

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

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

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

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

Advanced Techniques

Adaptive Self-Consistency

Create prompts that adjust based on initial consistency. You can play with it here

Adaptive Self-Consistency

---
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:

Note how we used structured outputs to capture the consistency check results and decide whether to generate additional samples.

Self-Consistency with Uncertainty Quantification

Implement self-consistency that quantifies uncertainty:

Uncertainty-Aware Self-Consistency

---
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>

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 - Break down complex problems into step-by-step reasoning
• Tree-of-Thoughts - Explore multiple reasoning paths systematically
• Few-Shot Learning - Use examples to guide AI behavior and improve consistency