Join us for a live webinar!

Enabling Safe and Reliable Generative AI for Financial Services: RAG Techniques and Challenges 

August 8, 2024, 9:00 am PST

The rapid advancement of Generative AI and Retrieval Augmented Generation (RAG) systems has opened up new possibilities for the financial services industry. However, implementing these technologies in a way that is both safe and reliable presents significant challenges.

Topics to be explored:

  • The current state of RAG systems in finance and key hurdles that financial institutions face.

  • The performance of different RAG systems on FinanceBench - a benchmark designed to test AI systems on real-world financial questions using company filings and documents.

  • Broader challenges around ensuring the accuracy, credibility, and safety of generative AI outputs in the financial domain.

  • Best practices for developing and deploying RAG systems and how Kolena can partner with organizations to enable safe and reliable generative AI
Whether you are a business leader looking to understand the potential of generative AI in finance, or a technical practitioner grappling with the challenges of implementation, this webinar will provide valuable insights and practical guidance.

Join us to learn how your organization can harness the power of RAG systems while ensuring the highest standards of quality and reliability.

This webinar has passed but you can watch it HERE!

Meet the speakers

Join our expert speakers for a presentation and Q&A

Gordon Hart Headshot

Gordon Hart

After being burned one too many times by unexpected model performance in mission-critical production scenarios, Gordon co-founded Kolena to fix fundamental problems with ML testing practices across the industry. 

Prior to Kolena, Gordon designed, implemented, and deployed computer vision products for defense and security as Head of Product at Synapse (acq. by Palantir) and at Palantir.

skip

Skip Everling

At Kolena, Skip's role as Head of Developer Relations. His objective is to help ML/AI engineers and data scientists more effectively test and refine their models so that they perform robustly in the real world.