RAG Quality Starts with Data Quality // Adam Kamor // #262

MLOps.community

Adam Kamor is the Co-founder of Tonic, a company that specializes in creating mock data that preserves secure datasets.

RAG Quality Starts with Data Quality // MLOps Podcast #262 with Adam Kamor, Co-Founder & Head of Engineering of Tonic.ai.

// Abstract

Dive into what makes Retrieval-Augmented Generation (RAG) systems tick—and it all starts with the data. We’ll be talking with an expert in the field who knows exactly how to transform messy, unstructured enterprise data into high-quality fuel for RAG systems. Expect to learn the essentials of data prep, uncover the common challenges that can derail even the best-laid plans, and discover some insider tips on how to boost your RAG system’s performance. We’ll also touch on the critical aspects of data privacy and governance, ensuring your data stays secure while maximizing its utility. If you’re aiming to get the most out of your RAG systems or just curious about the behind-the-scenes work that makes them effective, this episode is packed with insights that can help you level up your game.

// Bio

Adam Kamor, PhD, is the Co-founder and Head of Engineering of Tonic.ai. Since completing his PhD in Physics at Georgia Tech, Adam has committed himself to enabling the work of others through the programs he develops. In his roles at Microsoft and Kabbage, he handled UI design and led the development of new features to anticipate customer needs. At Tableau, he played a role in developing the platform’s analytics/calculation capabilities. As a founder of Tonic.ai, he is leading the development of unstructured data solutions that are transforming the work of fellow developers, analysts, and data engineers alike.

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// Related Links

Website: https://www.tonic.ai

Various topics about RAG and LLM security are available on Tonic.ai's blogs: https://www.tonic.ai/bloghttps://www.tonic.ai/blog/how-to-prevent-data-leakage-in-your-ai-applications-with-tonic-textual-and-snowpark-container-serviceshttps://www.tonic.ai/blog/rag-evaluation-series-validating-the-rag-performance-of-the-openais-rag-assistant-vs-googles-vertex-search-and-conversation

https://www.youtube.com/watch?v=5xdyt4oRONUhttps://www.tonic.ai/blog/what-is-retrieval-augmented-generation-the-benefits-of-implementing-rag-in-using-llms

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Timestamps:

[00:00] Adam's preferred coffee

[00:24] Takeaways

[00:59] Huge shout-out to Tonic.ai for supporting the community!

[01:03] Please like, share, leave a review, and subscribe to our MLOps channels!

[01:18] Naming a product

[03:38] Tonic Textual

[08:00] Managing PII and Data Safety

[10:16] Chunking strategies for context

[14:19] Data prep for RAG

[17:20] Data quality in AI systems

[20:58] Data integrity in PDFs

[27:12] Ensuring chatbot data freshness

[33:02] Managed PostgreSQL and Vector DB

[34:49] RBAC database vs file access

[37:35] Slack AI data leakage solutions

[42:26] Hot swapping

[46:06] LLM security concerns

[47:03] Privacy management best practices

[49:02] Chatbot design patterns

[50:39] RAG growth and impact

[52:40] Retrieval Evaluation best practices

[59:20] Wrap up