Research and evaluation framework for testing LLM behavior, cataloging techniques, and visualizing results through dashboards and mindmaps
As AI adoption accelerates, companies need repeatable evaluation and security testing for model behavior. This framework provides a structured approach to catalog techniques, record evaluations, and present results in a dashboard-friendly format suitable for research and decision making.
Organizations deploying AI systems need confidence that models behave safely, consistently, and effectively across different scenarios. Manual testing is time-consuming and non-repeatable. This framework provides a systematic approach to evaluate LLM behavior, document techniques, and visualize results for both technical teams and executive stakeholders.
Systematic approach to cataloging and testing LLM techniques with repeatable methodologies
Dashboard visualizations make complex AI research accessible to non-technical stakeholders
Technique taxonomy and evaluation database serve as foundation for ongoing AI research
Built to integrate with evaluation databases and automated testing pipelines
Interactive web interface for viewing evaluation statistics, browsing results, and exploring technique references.
Comprehensive catalog of LLM optimization techniques with structured documentation and examples.
Visual representation of techniques and relationships through interactive HTML mindmap navigation.
Automated scripts for generating test data, populating databases, and running evaluation batches.
Complete deployment and verification scripts for rapid setup and hosting of the evaluation platform.
Statistical analysis and visualization of evaluation results for informed decision making.
Complete web application in app_final.py with routes, templates, and data integration ready for deployment.
Comprehensive technique documentation in TECHNIQUES_REFERENCE.md with structured taxonomy and examples.
HTML-based mindmap visualization in techniques_mindmap.html with interactive navigation and relationship mapping.
Complete deployment automation in setup_mindmap.sh with environment configuration and verification tooling.
Research teams can systematically evaluate model behavior and catalog optimization techniques for ongoing development.
Dashboard visualizations provide non-technical stakeholders with insights into AI model performance and capabilities.
Organizations can evaluate model behavior under adversarial conditions and document security characteristics.
Discover how our LLM optimization framework can support your AI research and evaluation initiatives with structured methodologies and executive-friendly visualizations.