Evaluation Engineering for AI Systems

Building Reliable Evaluations from Benchmarks to Production

Introduction: Why Evaluation Is the Bottleneck

1. The Cost of Bad Evals: A Tale of Two Deployments
2. What Evaluation Engineering Actually Is
3. The Eval Crisis in Modern AI Development
4. How This Book Is Structured

Chapter 1: Foundations of AI Evaluation

1. From Accuracy to Alignment: How Evaluation Evolved
2. The Anatomy of an Evaluation
3. Where Evals Live in the Development Lifecycle
4. Internal vs External Validation
5. Key Takeaways

Chapter 2: Designing Effective Evaluations

1. Principles of Good Evaluation Design
2. Defining What Matters: Requirements-Driven Evaluation
3. Constructing Test Cases That Reveal Truth
4. The Golden Dataset Pattern
5. Key Takeaways

Chapter 3: Metrics for AI Systems

1. Classical Metrics Revisited
2. Text Generation Metrics
3. Semantics-Aware Metrics
4. Choosing the Right Metric for Your Task
5. Key Takeaways

Chapter 4: Benchmarks and Datasets

1. The Benchmark Ecosystem
2. How Benchmarks Become Obsolete
3. Data Contamination: The Silent Killer of Benchmarks
4. Building Custom Benchmarks That Outlast Trends
5. Key Takeaways

Chapter 5: Automated Evaluation at Scale

1. Rule-Based and Programmatic Evaluators
2. Model-Assisted Evaluation
3. Test Case Generation at Scale
4. Automation Pitfalls
5. End-to-End Evaluation Pipeline Example
6. Key Takeaways

Chapter 6: LLM-as-a-Judge

1. The Rise of LLM-as-a-Judge
2. Prompt Design for Reliable Judging
3. Known Failure Modes
4. Best Practices and Emerging Techniques
5. Key Takeaways

Chapter 7: Human Evaluation

1. When Humans Are Indispensable
2. Designing Rigorous Human Studies
3. Crowdsourcing vs Expert Review
4. Integrating Human Feedback Into Evals
5. Key Takeaways

Chapter 8: Statistical Rigor in Evaluation

1. Why Statistics Matter in Evals
2. Confidence Intervals for Model Scores
3. Significance Testing: Did Your Change Actually Help?
4. Effect Sizes and Practical Significance
5. Key Takeaways

Chapter 9: Error Analysis and Iteration

1. Systematic Error Analysis
2. From Errors to Features
3. Case Study: Iterating on a Production System Through Error Analysis
4. Key Takeaways

Chapter 10: Regression Testing and Continuous Evaluation

1. Eval as Code
2. CI/CD Integration
3. Regression Detection
4. Key Takeaways

Chapter 11: Production Monitoring and Observability

1. From Offline to Online Evaluation
2. Production Metrics That Matter
3. Drift Detection
4. Building an Observability Stack for AI
5. Key Takeaways

Chapter 12: Evaluating Agents and RAG Systems

1. Why Agents Break Standard Evals
2. Evaluating RAG Systems
3. Agent Evaluation Frameworks
4. Tools and Patterns for Agent/RAG Evals
5. Key Takeaways

Chapter 13: Safety, Robustness, and Adversarial Evaluation

1. Evaluating for Hallucinations
2. Bias and Fairness Evaluation
3. Adversarial Testing and Jailbreak Detection
4. Robustness Under Perturbation
5. Key Takeaways

Chapter 14: Multimodal and Specialized Evaluation

1. Evaluating Vision-Language Models
2. Code Generation Evaluation
3. Audio and Speech Evaluation
4. Cross-Modal Alignment
5. Key Takeaways

Chapter 15: Cost, Tradeoffs, and Scaling Eval Infrastructure

1. The Economics of Evaluation
2. Cost-Performance Tradeoffs
3. Building Eval Infrastructure at Scale
4. Design Patterns and Anti-Patterns
5. Case Study: Scaling Evaluation Infrastructure at a Mid-Sized AI Company
6. Key Takeaways

Conclusion: The Future of Evaluation Engineering

1. Where We Are Now
2. Emerging Frontiers
3. The Maturation of Eval as a Discipline
4. Final Thoughts: Building Trust Through Evaluation

References