Generative AI Evaluation Essentials

Target Audience

The tutorial is designed for AI researchers, data scientists, software engineers, and technical professionals in industry, government, and academic sectors developing, working with, or purchasing GenAI technologies. Ideal participants have basic knowledge of GenAI and its applications and an interest in evaluating, testing, and monitoring AI systems. The content will be particularly valuable for those responsible for AI system design, deployment, risk management, and quality assurance.

Expected Outcomes

Participants will develop a comprehensive knowledge of current GenAI testing and evaluation methodologies, learning how different evaluation tools interact and when to apply each appropriately. With the tutorial, participants will understand how to customize GenAI testing and implement continuous performance monitoring strategies. Participants will gain insight into the importance of sustaining evaluation tools across use cases and product evolution.

Tutorial Outline

1. Introduction

It will begin with key GenAI concept definitions and outline the scope of the tutorial. This will be followed by an overview of current GenAI use in consumer, enterprise, and government, along with the state of testing across these use cases.

2. Exploring the interplay of AI evaluation tools

In AI evaluation, an array of tools exists to evaluate systems. From metrics and benchmarks to red-teaming, these methodologies offer critical insights into the capabilities, limitations, and potential risks of GenAI systems.

• Overview
• Purpose of an evaluation
• Different evaluation tools: metrics, benchmarks, testing, red-teaming, and auditing.
• Exploring the different tools
• Common tools: metrics, benchmarks, model testing, field-testing, and red-teaming.
• Strengths and weaknesses
• Typical use
• Recommendations for using evaluation tools
• Prioritization and triage
• Customization
3. Sustaining AI Evaluation tools and practices

AI evaluation tools can be dynamic systems requiring sustainment. Every system, with or without AI, has failure modes, reliability, and quality concerns. GenAI evaluation tools demand ongoing reliability assessment and scrutiny for quality.

• Key systems engineering concepts
• Reliability
• Failures and failure modes
• GenAI-specific reliability concerns
• Example: Benchmark reliability efforts
• Identifying benchmark failure modes
• Saturation of the BBQ benchmark
• AILuminate was built with benchmark integrity and sustainment in mind.
4. Creating and Sustaining Evaluation Datasets

The next component of this tutorial explores how to conceptualize, build, and refine prompt datasets for evaluating GenAI systems. Challenges include building data standards for test data collection and creation across the data life-cycle:

• Systematic processes and structures
• Ongoing data tuning including feedback, improvements, and refinement
• Anticipating data distribution imbalances
• Identifying protected classes in collected data
• Centering human-evaluator-based best practice
• Example: Low-resource languages
• Common challenges
• Devising and building evaluation datasets
• Present specific strategies from low-resource conditions that are extensible to AI testing
5. Deploying assessments for system monitoring and adaptation
The final part of the tutorial focuses on applying the practices from the previous sections to real-world AI evaluation using a complex systems approach. The practicum will walk participants through a real-world agentic GenAI deployment, demonstrating how to:
• Translate human objectives to GenAI key performance indicators
• Collect implicit evaluation data from experts
• Evaluate continuously in production environments
• Triage results and create pipelines for adaptation (improvement, rare events, and evolution)
6. Summary and recommendations

The tutorial will conclude with a summary and recommendations for future research.