RAG Quality Starts with Data Quality // Adam Kamor // #262
Sep 20, 2024
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In this engaging discussion, Adam Kamor, co-founder of Tonic, shares his expertise in creating mock data while ensuring data privacy. He highlights the significance of high-quality data for Retrieval-Augmented Generation (RAG) systems, tackling challenges like data documentation and chunking. Adam emphasizes innovative strategies for managing sensitive information and maintaining accuracy in retrieval. Listeners will gain valuable insights into building effective data pipelines and the critical role of database tools in today’s AI landscape.
Data quality is foundational for effective retrieval-augmented generation (RAG) systems, directly affecting the accuracy of AI-generated responses.
Organizations face significant challenges in maintaining accurate employee documentation, which undermines the utility of available enterprise data.
Tonic Textual utilizes advanced techniques like named entity recognition for sensitive data management, ensuring data privacy while enhancing RAG system performance.
Deep dives
Understanding Tonic Textual
Tonic Textual is designed to build high-quality data pipelines specifically for retrieval-augmented generation (RAG) systems. Its development stemmed from the realization that data quality is crucial for generating accurate answers in AI-driven applications. Quality data enhances the context available when querying sensitive data, which is essential for ensuring appropriate responses. This is particularly important in environments where data management involves handling personally identifiable information (PII).
Challenges of Data Documentation
One persistent issue within organizations is the challenge of employee documentation—many struggle to maintain accurate records, which detrimentally affects data utility. Despite the abundance of available data, the need for proper documentation is often overlooked, leading to ineffective systems that are incapable of producing quality answers. Tonic Textual addresses this factor by emphasizing the importance of quality data input as the foundation for effective data retrieval. The tool offers mechanisms to improve documentation practices and enhance the overall data pipeline.
Data Sensitivity and Entity Recognition
Tonic Textual employs named entity recognition (NER) models to identify sensitive information within unstructured text to prevent data leakage. The models effectively analyze incoming data to detect both sensitive entities, such as social security numbers, and potentially useful ones, such as product names within transcripts. Through this processing, the system can either redact sensitive data or synthesize it, ensuring that it remains high quality while being safe to use within RAG systems. This level of careful management around data sensitivity is essential for organizations looking to leverage AI effectively without risking privacy violations.
Chunking Techniques and Data Extraction
Effective chunking techniques are critical in managing how data is processed and how context is preserved within RAG systems. Tonic Textual supports various chunking algorithms, enabling users to integrate their own tailored solutions or work with default options provided by the tool. These varied strategies ensure that different document types, such as FAQs or complex reports, are optimally processed for specific inquiries. This versatility allows organizations to dynamically adapt their systems to meet unique operational demands.
Quality Control and Evaluation Metrics
To maintain high data quality and system effectiveness, Tonic Textual incorporates evaluation metrics that assess both the quality of retrieved context and the accuracy of generated responses. Among the metrics used are answer similarity, which compares AI-generated answers to human-provided reference answers, and context relevance metrics that determine the pertinence of the retrieved information. Such comprehensive evaluation techniques help organizations track performance trends, ensuring that the AI system continues to produce reliable results, especially as RAG applications evolve from internal tools to critical external-facing solutions.
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/blog
https://www.tonic.ai/blog/how-to-prevent-data-leakage-in-your-ai-applications-with-tonic-textual-and-snowpark-container-services
https://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=5xdyt4oRONU
https://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
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