Evaluating AI Systems: Testing LLMs, RAG, and Agents
Published by Self-published · Tech
A guide to properly testing and evaluating AI systems, including benchmarking strategies for LLMs, RAG pipelines, and agents.
Who this book is for
AI and ML engineers, QA engineers, platform engineers, technical leaders, and data engineers who need systematic evaluation for AI systems. No prior evaluation experience is assumed, though basic Python, SQL, and CI/CD familiarity helps.
Table of contents
- Why Evaluation Is Different for AI
- Evaluation Fundamentals
- Prompt Testing
- Dataset Generation
- RAG Evaluation
- Hallucination Detection
- Agent Evaluation
- LLM-as-Judge Patterns
- Human Evaluation
- Evaluation Infrastructure
- Regression Testing and Monitoring
- Building an Evaluation Culture
Excerpt
AI systems do not work this way. Ask an LLM to summarize a quarterly earnings report and you may get a different summary each time. Both summaries might be accurate. One might emphasize revenue growth while the other leads with margin expansion. Both are correct, but they are not identical. Traditional assertion-based testing cannot handle this. This chapter establishes why AI systems require a fundamentally different approach to quality assurance, what that approach looks like at a high level, and what happens when teams skip it.
