Evaluation of LLMs and LLM Applications
Presentation download
Today, I had the opportunity to present at Analytics Vidhya, focusing on the pragmatic aspects of evaluating Large Language Models (LLMs) and LLM-based systems. Rather than delving into academic theories, my presentation emphasized the design of effective evaluation processes and metrics. These are crafted to empower science and engineering teams, enabling them to develop applications that yield the most significant benefits for both customers and businesses.
My discussion covered the essential elements of the typical evaluation process, highlighting how it should be structured to allow science teams to fine-tune LLMs efficiently, thereby maximizing their impact on business outcomes. I also provided an overview of the evaluation metrics landscape, discussing how these can be adapted for specific use cases and products to assess LLMs accurately.
The focus was on principles and examples of robust evaluation practices that are relevant across a broad spectrum of business sectors. I examined the comprehensive evaluation of end-to-end LLM-based systems, addressing not only the NLP aspects but also the production and operational dimensions. Additionally, I reviewed open-source software that is instrumental in constructing evaluation suites.
Condensing this vast subject into an 80-minute presentation was a challenge; indeed, a more extensive, six-hour class series would be ideal to delve into the practicalities and impart actionable knowledge for developing LLM systems.
Looking ahead, I plan to expand my presentations to cover Generative AI applications in search, recommendation, and personalization, among other areas of high demand and interest.
I’ll share the video of the recording once it’s available
