RAG Quality Starts with Data Quality - Enhancing Retrieval-Augmented Generation Systems

Overview

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Explore the critical role of data quality in Retrieval-Augmented Generation (RAG) systems through this insightful podcast episode featuring Adam Kamor, Co-Founder & Head of Engineering of Tonic.ai. Delve into essential aspects of data preparation, common challenges in RAG implementation, and expert tips for enhancing system performance. Learn about transforming unstructured enterprise data into high-quality input for RAG systems, while addressing crucial data privacy and governance concerns. Gain valuable insights on maximizing RAG effectiveness, managing personally identifiable information (PII), implementing chunking strategies, ensuring data freshness in chatbots, and evaluating retrieval performance. Discover best practices for LLM security, privacy management, and chatbot design patterns, as well as the growing impact of RAG in AI applications.

Syllabus

[] Adam's preferred coffee
[] Takeaways
[] Huge shout out to Tonic.ai for supporting the community!
[] Please like, share, leave a review, and subscribe to our MLOps channels!
[] Naming a product
[] Tonic Textual
[] Managing PII and Data Safety
[] Chunking strategies for context
[] Data prep for RAG
[] Data quality in AI systems
[] Data integrity in PDFs
[] Ensuring chatbot data freshness
[] Managed PostgreSQL and Vector DB
[] RBAC database vs file access
[] Slack AI data leakage solutions
[] Hot swapping
[] LLM security concerns
[] Privacy management best practices
[] Chatbot design patterns
[] RAG growth and impact
[] Retrieval Evaluation best practices
[] Wrap up