In a world where AI assists in critical business decisions, accuracy isn’t optional. Yet, data lags and outdated information can disrupt strategies, making real-time relevance essential for companies. This webinar dives into the practical solutions leading companies use to bridge these data gaps, including Retrieval Augmented Generation (RAG) for up-to-date, reliable AI results.
Topics Covered:
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.
Speakers
- Seth Earley
CEO and Founder, Earley Information Science - Patrick Hoeffel
Managing Partner, Patrick Hoeffel Partners - Sanjay Mehta
Principal Architect, Earley Information Science - Trey Grainger, Founder, Searchkernel
