About This Project
A comprehensive LLM response quality analyzer built for the GenAI Labs Challenge. Understand how temperature and top_p parameters affect response characteristics through data-driven metrics.
Quality Metrics Explained
Coherence Score
Measures logical flow and topic consistency within the response
Algorithm:
- Analyzes sentence transitions and word overlap between consecutive sentences
- Detects transition words and phrases (however, therefore, furthermore, etc.)
- Calculates semantic similarity using word overlap ratios
- Awards bonus points for consistent paragraph structure
Interpretation:
Completeness Score
Measures how well the response addresses all aspects of the prompt
Algorithm:
- Extracts key terms from the prompt (nouns, verbs, important concepts)
- Checks if response addresses these key terms
- Looks for common response patterns (examples, lists, explanations)
- Measures depth through response patterns and structure
Interpretation:
Readability Score
Measures how easy the text is to read and understand
Algorithm:
- Uses Flesch Reading Ease formula (industry-standard readability metric)
- Measures sentence length variance and complexity
- Evaluates vocabulary complexity through syllable counting
- Penalizes overly long sentences (> 40 words)
Interpretation:
Length Appropriateness
Evaluates if the response length matches the prompt requirements
Algorithm:
- Estimates expected response length based on prompt complexity
- Analyzes prompt type (question, explanation, list, etc.)
- Adjusts expectations for different question types
- Penalizes responses that are too short (incomplete) or too verbose
Interpretation:
Structural Quality
Measures formatting, organization, and presentation quality
Algorithm:
- Checks for proper paragraph breaks and spacing
- Validates list formatting and consistency
- Looks for code blocks, headers, and structural elements
- Evaluates punctuation consistency and balanced syntax
Interpretation:
Key Features
Everything you need to analyze and compare LLM responses
Technology Stack
Built with modern, production-ready technologies
Frontend
State Management
UI Components
Backend
LLM Integration
Data Export
GenAI Labs Challenge 2025
A full-stack demonstration of LLM parameter analysis and quality metrics
This application was developed as part of the GenAI Labs Challenge to demonstrate expertise in full-stack development, LLM integration, data analysis, and UI/UX design. The project showcases the ability to build production-ready applications that solve real-world problems in the AI space.