DataFramed
#271 Creating High Quality AI Applications with Theresa Parker & Sudhi Balan, Rocket Software
Jan 2, 2025
Theresa Parker, a seasoned technology executive with 25 years of experience, and Sudhi Balan, the CTO for AI & Cloud at Rocket Software, delve into the challenges of creating high-quality AI applications. They discuss retrieval augmented generation's role in enhancing text quality and its practical applications in customer support. The duo also highlights the significance of robust data governance, innovative testing strategies, and the transformative potential of AI in streamlining processes like loan approvals and improving customer service.
51:28
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Quick takeaways
- Retrieval Augmented Generation (RAG) enhances AI-driven applications by efficiently drawing accurate contextual information from extensive databases for improved text generation.
- Effective integration of AI into existing frameworks requires clear objectives, strategic planning, and a focus on proper data governance and user access controls.
Deep dives
Understanding Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) combines the strengths of retrieval-based models with generation-based models to enhance the production of quality text. This technique is leveraged to draw from extensive databases, making it especially effective in scenarios that necessitate accurate contextual information. RAG finds utility in various applications, including customer support, where it aids representatives by extracting precise answers from myriad manuals rather than requiring them to sift through extensive documents. By performing tedious tasks like analyzing customer incident logs, the AI enables human workers to focus on more valuable activities, thus leading to increased overall productivity.
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