Evaluate AI Systems
Summary
A recap of system evaluation principles, model selection trade-offs, and practical guidelines for building production evaluation harnesses.
Chapter Summary
Reliable evaluation is the single greatest catalyst for real-world AI adoption. While constructing evaluation pipelines demands up-front discipline, it mitigates catastrophic risks, accelerates iteration, and grounds development in quantifiable business value.
Core Chapter Takeaways
Application-Driven Criteria
Evaluation must always be grounded in specific application requirements. Criteria span domain-specific capabilities, generation quality (factual consistency and safety), instruction-following fidelity, and cost/latency economics.
Build vs. Buy Trade-offs
Choosing between proprietary model APIs and self-hosting open weights requires evaluating seven axes: data privacy, data lineage, capability frontier, tooling features, cost curves, version control, and on-device requirements.
Benchmark Realism & Contamination
Public benchmarks help filter out weak models but cannot identify the best candidate for your workload. Beware of unweighted averages, benchmark correlation, and widespread data contamination in web-scraped training sets.
Robust Pipeline Architecture
Evaluate systems across components, turns, and overarching tasks. Use slice-based evaluation to avoid Simpson's paradox, establish clear scoring rubrics, and calibrate automated evaluators against business KPIs.
Looking Ahead
Evaluation is not a one-time milestone; it is an ongoing practice that underpins every phase of AI engineering:
- Chapter 5: Prompt engineering and advanced reasoning techniques.
- Chapter 6: Retrieval-Augmented Generation (RAG) and agentic tool evaluation.
- Chapters 7 & 9: Fine-tuning, quantization, latency optimization, and memory profiling.
- Chapter 8: Synthetic data generation and automated quality verification.
- Chapter 10: Production observability, continuous monitoring, and user feedback loops.