Welcome to the fourth chapter of The Hitchhiker’s Guide to LLMs for Events. Here’s what you’ll learn:

• Definition of LLM evaluation and its importance in understanding model strengths, limitations and appropriate use cases
• Comparison of traditional ML evaluation methods with those required for foundation models like LLMs
• Holistic evaluation frameworks, key metrics and benchmark datasets
• Examines RLHF fine-tuned model evaluation and emerging techniques such as self-reflection and LLM-as-Judge
• Domain-specific evaluation challenges and the pitfalls of over-relying on benchmarks
• How to integrate evaluation into CI/CD pipelines using rule-based and model-graded approaches
• Operational considerations including cost, memory, latency and sequence length parameters

By the end of this chapter, you’ll be equipped with a clear understanding of how to design effective LLM evaluation strategies tailored to your specific goals and environments.

NOTE: This technical guide is designed for experts and professionals with some understanding of the relevant science.