Research Methodology - SNEOS AI Comparisons

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Research Methodology: SNEOS AI Comparison Framework

This document explains our systematic approach to comparing AI models, ensuring reliable, transparent, and academically rigorous evaluations across 2100+ comparisons.

🎯 Methodology Overview

Core Principles

Systematic Evaluation - Standardized prompts and evaluation criteria
Transparency - All prompts and responses publicly available
Reproducibility - Comparisons can be replicated by anyone
Bias Awareness - Acknowledge limitations and potential biases
Continuous Improvement - Regular methodology updates

Research Questions

Our comparison framework addresses:

Which AI model performs best for specific tasks?
What are the strengths and limitations of each model?
How do models compare across different use cases?
Which model offers best value for specific users?

📊 Evaluation Framework

1. Prompt Design

Standardization:

Each comparison uses identical prompts across all models
Prompts designed to test specific capabilities
Scenarios reflect real-world use cases
Complexity calibrated to task requirements

Prompt Categories:

Category	Example	Purpose
Factual Knowledge	"Explain quantum entanglement"	Test accuracy & depth
Analytical Reasoning	"Compare approaches to..."	Test logic & synthesis
Creative Generation	"Write a research proposal..."	Test creativity & originality
Technical Skills	"Write Python code for..."	Test domain expertise
Ethical Reasoning	"Analyze ethical implications..."	Test moral reasoning

2. Model Testing Protocol

Test Environment:

Same date and time for all models (when possible)
Default model settings (temperature, etc.)
No fine-tuning or custom instructions
Fresh conversation context

Models Evaluated:

ChatGPT (GPT-4 series) - OpenAI
Claude (Sonnet/Opus series) - Anthropic
Gemini (Pro/Advanced series) - Google
Grok - xAI
DeepSeek - DeepSeek
Mistral AI - Mistral

Version Tracking:

Model versions documented when available
Comparisons dated to reflect model capabilities at time of testing
Major version changes trigger re-evaluation

3. Evaluation Dimensions

Academic Research Criteria

Accuracy & Factual Correctness

Factual accuracy (verified against authoritative sources)
Citation accuracy (when provided)
Acknowledgment of uncertainty
Handling of controversial topics

Depth & Comprehensiveness

Level of detail
Coverage of relevant aspects
Integration of multiple perspectives
Handling of complexity

Analytical Quality

Logical coherence
Critical thinking
Evidence-based reasoning
Recognition of limitations

Methodological Soundness

Research design appropriateness
Statistical reasoning
Recognition of confounds
Ethical considerations

Writing Quality

Clarity and organization
Academic tone and style
Grammar and mechanics
Citation formatting (when applicable)

Practical Considerations

Usability

Response time
Ease of understanding
Actionability
Follow-up question handling

Versatility

Cross-domain performance
Adaptation to user needs
Handling of ambiguity

Value

Cost vs. performance
Access and availability
Rate limits and restrictions

🔬 Quality Assurance

Internal Validation

Multi-Reviewer Approach:

Comparisons reviewed by multiple team members when possible
Domain experts consulted for specialized topics
Peer review process for major comparisons

Consistency Checks:

Cross-comparison consistency
Temporal stability (re-testing over time)
Inter-rater reliability for subjective evaluations

External Validation

Community Feedback:

GitHub repository for issue reporting
User comments and corrections
Expert review solicitation

Reproducibility:

All prompts publicly available
Anyone can re-run comparisons on SNEOS.com
Encourage independent verification

📝 Data Collection & Analysis

Data Structure

Each comparison includes:

- Unique ID
- Date of comparison
- Prompt text
- Model responses (complete, unedited)
- Model versions (when available)
- Category/tags
- Comparison metadata

Analysis Approach

Qualitative Analysis:

Thematic analysis of response patterns
Identification of model-specific strengths
Pattern recognition across use cases
Critical incident identification

Quantitative Metrics (where applicable):

Response length
Response time
Factual accuracy scores
Code functionality (for programming tasks)

🎯 Use Case Categorization

Academic Use Cases

Literature Review (150+ comparisons)

Search strategy development
Paper summarization
Synthesis across sources
Gap identification

Data Analysis (200+ comparisons)

Statistical analysis
Qualitative coding
Visualization
Interpretation

Academic Writing (250+ comparisons)

Structure and organization
Clarity and style
Argument development
Citation management

Research Design (100+ comparisons)

Methodology selection
Study design
Sampling strategies
Ethical considerations

Legal Research (75+ comparisons) Medical Research (100+ comparisons) Business Analysis (150+ comparisons) Technical Documentation (100+ comparisons) Content Creation (200+ comparisons)

Plus 75+ other categories

⚖️ Limitations & Biases

Acknowledged Limitations

Prompt Dependency:

Results depend on specific prompts used
Different phrasings may yield different results
No single prompt can fully capture capability

Temporal Limitations:

Models continuously updated
Comparisons reflect specific point in time
Regular updates needed

Evaluator Subjectivity:

Some criteria require subjective judgment
Reviewer expertise and perspective matter
Inter-rater reliability not perfect

Resource Constraints:

Cannot test all possible use cases
Time and cost limitations
Focus on English language primarily

Potential Biases

Selection Bias:

Categories reflect perceived user interest
May not cover all niche use cases
Platform bias (testing on public interfaces)

Confirmation Bias:

Risk of seeing what we expect to see
Mitigated through structured evaluation
External review encouraged

Recency Bias:

Newer models may receive more attention
Historical comparisons may be outdated
Regular re-evaluation needed

🔄 Methodology Evolution

Version History

v1.0 (2024) - Initial framework

Basic prompt-response comparison
Qualitative evaluation
100 comparisons

v2.0 (2024) - Enhanced framework

Standardized evaluation dimensions
Category development
1000+ comparisons

v3.0 (2025) - Academic focus

Research-specific criteria
Systematic documentation
2100+ comparisons
Academic context wrapper

Future Improvements

Planned Enhancements:

Blind evaluation protocols
External expert validation
Quantitative scoring systems
Multilingual comparisons
Automated testing pipelines

📚 Citing Our Methodology

If you use our comparisons in academic work, please cite:

APA Format

SNEOS Team. (2025). Research methodology: SNEOS AI comparison
framework. SNEOS AI Library.
https://share.sneos.com/ai-library/researchers/methodology/

MLA Format

SNEOS Team. "Research Methodology: SNEOS AI Comparison Framework."
SNEOS AI Library, 2025,
share.sneos.com/ai-library/researchers/methodology/.

Chicago Format

SNEOS Team. "Research Methodology: SNEOS AI Comparison Framework."
SNEOS AI Library. 2025.
https://share.sneos.com/ai-library/researchers/methodology/.