An intelligent Streamlit application that acts as your personal consultant for LLM evaluation strategies. Get personalized recommendations based on cutting-edge research and industry best practices.
This tool helps you design effective evaluation strategies for Large Language Models (LLMs) by asking key questions about your project and providing tailored recommendations. Built using the latest research findings and proven methodologies from leading AI companies.
- Project Description: Describe your specific LLM evaluation needs
- Task Classification: Identify whether your task is objective or subjective
- System Purpose: Choose between evaluator vs. guard rails systems
- Baseline Comparison: Define what you're comparing against
- Latency Requirements: Specify your performance constraints
- Evaluation Approach: Direct scoring, pairwise comparison, or hybrid methods
- Recommended Metrics: Classification metrics vs. correlation metrics
- Implementation Strategy: Step-by-step guidance for your specific use case
- Key Considerations: Important factors based on your configuration
- Timeline: Suggested implementation phases
- Configuration Analysis: Identifies optimal vs. challenging setups
- Best Practices: Research-backed recommendations
- Resource Links: Curated learning materials and tools
- Exportable Reports: Download your complete strategy as Markdown
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Clone the repository:
git clone <repository-url> cd llm-evals-consultant
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Install dependencies:
pip install -r requirements.txt
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Run the application:
streamlit run app.py
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Open in browser: Navigate to
http://localhost:8501
Fill out the questionnaire in the sidebar with details about your LLM evaluation project. Be specific about:
- What type of content you're evaluating
- What aspects of quality matter most
- Your current evaluation challenges
The tool asks five critical questions:
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Task Nature:
- Objective: Clear right/wrong answers (factual accuracy, policy violations)
- Subjective: Opinion-based assessments (helpfulness, creativity, tone)
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System Purpose:
- Evaluator: Offline assessment for model improvement
- Guard Rails: Real-time filtering in production
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Current Baseline: What you're comparing against
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Latency Requirements: How fast decisions need to be made
Click "Generate Strategy" to receive:
- Tailored evaluation approach
- Specific metrics to track
- Implementation timeline
- Strategic considerations
- Next steps and resources
Scenario: Evaluating customer service chatbot responses for helpfulness and accuracy Recommendation: Pairwise comparison with correlation metrics like Cohen's κ
Scenario: Real-time detection of harmful content Recommendation: Direct scoring with binary classification, consider finetuned models for production
Scenario: Evaluating AI-generated code for correctness Recommendation: Direct scoring with precision/recall metrics
Scenario: Assessing generated stories for creativity and engagement Recommendation: Pairwise comparison with human alignment metrics
The recommendations are based on extensive research findings from leading AI institutions:
- Direct Scoring: Best for objective tasks and single-response evaluation
- Pairwise Comparison: More reliable for subjective assessments
- Reference-based: When you have gold standard examples
- Classification Metrics: Precision, Recall, F1-Score for objective tasks
- Correlation Metrics: Cohen's κ, Kendall's τ, Spearman's ρ for subjective tasks
- Cohen's κ: 0.21-0.40 = Fair agreement, 0.41-0.60 = Moderate agreement
- Human Baseline: Target 0.6-0.8 correlation for subjective tasks
- Speed vs. Accuracy: LLM APIs for accuracy, finetuned models for speed
- Prompting: Chain-of-Thought + few-shot examples improve reliability
This tool synthesizes insights from:
- Leading AI research institutions and labs
- Industry best practices from major tech companies
- Peer-reviewed papers on LLM evaluation methodologies
- Real-world deployment experiences and case studies
- G-Eval: NLG Evaluation Using GPT-4 with Better Human Alignment
- MT-Bench: Multi-turn conversation evaluation benchmarks
- Constitutional AI: AI systems with built-in safety measures
- LLM-as-a-Judge: Using language models for evaluation tasks
This tool is designed to evolve with the rapidly advancing field of LLM evaluation. Contributions welcome for:
- New evaluation strategies
- Updated research insights
- Additional use case examples
- UI/UX improvements
- Research-Based: Recommendations reflect current best practices but should be validated for your specific use case
- Rapidly Evolving Field: LLM evaluation techniques are advancing quickly
- Context Matters: Consider your domain, data, and constraints when implementing recommendations
- Start Simple: Begin with basic approaches and iterate based on real-world performance
This project is built for educational and research purposes, incorporating insights from publicly available research and best practices in LLM evaluation.
Built with ❤️ using Streamlit | Advanced LLM Evaluation Consulting System