Self-Consistency

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:

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
Answer Extraction: Each response is analyzed to extract the core answer or classification, regardless of the reasoning path taken
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:

---
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:

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

Logic and Reasoning Self-Consistency

Use self-consistency for complex logical problems:

---
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:

---
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

Consistency Analysis

Problem Selection

Performance Optimization

Advanced Techniques

Adaptive Self-Consistency

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

---
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.