Retrieval-Augmented Generation (RAG) and chains have quickly emerged as leading methods for developing context-aware GenAI applications, yet they still suffer from hallucinations. The complexity of RAG’s many components, from retrieval and generation mechanisms to embeddings, makes debugging and optimizing these systems particularly challenging.
Watch for our webinar with Pinecone to learn:
- Strategies for identifying and mitigating hallucinations in RAG systems
- How to leverage vector databases for enhanced context
- Ways to utilize RAG and chain analytics for rapid iteration and improvement, including a hands-on example of production debugging
- Roie Schwaber-Cohen is a Staff Developer Advocate at Pinecone, a leading vector database company powering state-of-the-art GenAI applications.
- Quique Lores is the Head of Product at Galileo, a GenAI Ops company focused on accelerating evaluation, experimentation, and observability for Generative AI applications. Prior to joining Galileo, Quique was a Software Engineer at Google.