Catalog & Cocktails: The Honest, No-BS Data Podcast Data Quality: The Key to GenAI Success with Kevin Hu
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Oct 3, 2024 Kevin Hu, CEO & Co-Founder of Metaplane, dives into the critical role of data quality in Generative AI. He discusses how unstructured data complicates AI processes and the need for effective governance to ensure reliable data applications. Kevin emphasizes the importance of qualitative approaches to assess data quality and the cultural shift required to maintain integrity. With engaging metaphors and real-world examples, he sheds light on how businesses can adapt to the evolving landscape of data management.
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Gen AI's Unique Data Challenges
- Gen AI introduces significant complexity in data quality due to its non-deterministic nature and unstructured data use.
- Unlike traditional BI or ML, most Gen AI applications operate without human oversight, increasing quality challenges.
Approach to Gen AI Data Quality
- Use existing data governance practices for structured data while developing new methods for unstructured data in Gen AI.
- Apply validation tests, use LMs to evaluate outputs, and monitor embeddings quality for AI systems.
Customer Query as Readiness Test
- Allowing customers to query data directly is a key litmus test for data readiness in Gen AI.
- If you're uncomfortable with customers querying raw data, you shouldn't allow autonomous LMs to do so without human checks.