Is My AI Assistant Lying to Me? Accuracy in Generative AI
Webinar Series - Session 4
AI and Search: Navigating the Future of Business Transformation
Summary
Data Access and Security
User permissions and robust authentication measures are critical for secure access to company data when using Language Learning Models (LLMs). Ensuring that generative models only access documents that users are authorized to view, through techniques like "security trimming," is essential for maintaining data privacy and integrity.
Information Architecture and Data Integration
Inconsistencies in metadata fields can create significant challenges when integrating data from multiple sources. The use of a shared dictionary and organized information architecture is necessary to overcome these challenges. Understanding and standardizing the terminology and vocabulary across data sources is vital for seamless data integration and retrieval.
Use Cases and Testing
Initiating AI projects with clear, specific use cases that can be tested and validated is crucial. These use cases provide metrics to measure success and help in understanding the completeness and quality of data sources. Content and knowledge architecture must be maintained accurately to ensure precise data retrieval.
Technology Deployment
The deployment of LLMs for backend data enrichment offers low-risk opportunities to test AI technologies. Algorithms can significantly enhance data quality through enrichment and curation. Future advancements in information retrieval will rely heavily on strong information architecture that supports these models.
Governance and Data Maintenance
Effective governance is essential for maintaining data accuracy and updating processes. Testing in controlled environments is recommended to gain valuable insights and feedback. Ensuring the quality and structure of the knowledge architecture is critical for the successful implementation of AI technologies.
These themes underscore the importance of a structured approach to implementing AI, emphasizing robust data management, security, and an organized information architecture to support the evolving capabilities of LLMs and other AI technologies.
We're excited to continue the discussion. Be sure to review upcoming webinars in this series.
Session Highlights
"Understanding how RAG works, retrieval, augmented generation, really, which is to me the keys to the kingdom that is the most important thing that is going to happen to any organization in the near future." - Seth Earley
"One of the great opportunities that generative AI offers us is if you think about that 300 page manual that's in on your desk and you have to read through that 300 page manual and pull out information and categorize it and tag it and find those pieces of relevant content that somebody might need. It's, it's a little bit overwhelming to think about, gosh, how am I going to go through not just this manual, but 50 more behind it? An LLM can read that document and extract for you a lot of these question answer pairs, a lot of this categorized information." - Patrick Hoeffel
"The real goal of a RAG system is to be able to understand the full context of the content, the full context of the user and the full context of the domain." - Trey Grainger
"You might have heard of tokens and how many tokens can each model and they're increasing exponentially as we speak. But that again is how much history do you retain too? And how valuable and how valuable is more recent history versus the historical. Basically how stale does this data and the history get? I think is something to think about." - Sanjay Mehta