Evaluating Retrieval-Augmented Generation (RAG)
What will you learn?
Retrieval-Augmented Generation (RAG) has emerged as a cornerstone technology powering the latest wave of Generative AI applications, from sophisticated question-answering systems to advanced semantic search engines. As RAG's popularity has grown, we've witnessed a proliferation of methods promising to enhance the traditional RAG pipeline. These innovations include query rewriting, intelligent routing, and result reranking—but how do we measure their real impact on application performance?
Join us for an informational webinar where we'll explore robust evaluation frameworks, including LLM-as-a-Judge methodologies, industry-standard benchmarking datasets, and innovative synthetic data generation techniques. By the end of this session, you'll master practical approaches to evaluate and optimize RAG systems, equipped with the knowledge to implement these tools effectively in your own applications.
Topics covered:
LLM-as-a-Judge
MT-Bench
LM Eval Harness
Synthetic data generation
Speaker: Stefan Webb, DevRel at Zilliz