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 our webinar with Galileo 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
This was an informative session for any AI builders looking for frameworks and tools to optimize RAG and LLM performance!