Master the Art of Evaluating Large Language Model Applications Across Their Lifecycle
In the ever-evolving world of AI, robust evaluation is key to building reliable, high-performing large language model (LLM) applications. “The Definitive Guide to LLM App Evaluation” is your ultimate guide to mastering evaluation strategies that ensure your applications thrive from inception to deployment and beyond.
What You’ll Learn:
The Fundamentals of LLM Evaluation: Gain a clear understanding of evaluation’s role in measuring and improving LLM performance, from offline testing to real-time monitoring in production.
Types of Evaluation: Dive into diverse approaches, including code-based evaluations, LLM-as-a-judge techniques, and task-specific evaluation frameworks tailored to your application’s needs.
Online vs. Offline Evaluation: Learn when to use pre-deployment testing versus real-time evaluation to ensure consistent performance and capture dynamic, real-world feedback.
Benchmarking and Continuous Improvement: Discover how to create benchmarks, optimize evaluation models, and implement self-improving evaluations that adapt to evolving challenges.
Experimentation and CI/CD Integration: Explore how to design experiments, analyze results, and integrate evaluations into CI/CD pipelines for seamless application updates.
Best Practices for Dataset Creation: Master techniques for curating golden datasets, using synthetic data, and incorporating human feedback to enhance evaluation accuracy and reliability. Equip yourself with the tools and knowledge to confidently evaluate and optimize your LLM applications at every stage of their lifecycle.
