Part 2 of How to START with AI, now we focus on integration of external, real-time data in our company fine-tuned AI /LLM /VLM.
An in-depth exploration of how to effectively integrate a fine-tuned artificial intelligence system, particularly a large language model (LLM), with external data sources for enhanced functionality and real-world application.
Fine-Tuning and External Data Integration: The video begins by emphasizing the importance of fine-tuning a pre-trained LLM for specific tasks, analogous to customizing a vehicle for specific functions like an ambulance or a bus. This customization tailors the AI's capabilities to particular operational needs. The presenter then delves into the critical aspect of connecting this fine-tuned AI to external data sources, such as databases, data lakes, or sensor data. This connection is vital for the AI to interact effectively with its environment, such as an ambulance coordinating with a hospital in real-time or a bus syncing with transportation schedules.
Technical Aspects of AI Integration and Data Retrieval: The presenter discusses various technical methods for integrating AI with external data, including using SQL prompts for database queries and handling different types of data (structured and unstructured). A significant focus is placed on the concept of Retrieval Augmented Generation (RAG), a method of enhancing AI's response quality by retrieving relevant external data. The video explains how RAG can be implemented in different scenarios, introducing brand new tools like "Databricks RAG" for creating production-ready high-quality RAG applications. The presenter also differentiates between situations where simple keyword searches are sufficient and those requiring more complex vector searches, using examples from different scientific domains.
Challenges and Considerations in AI Data Integration: The final part of the video addresses the challenges and considerations in AI data integration. It covers the nuances of creating effective vector spaces for data retrieval, the risks associated with vector embeddings and data privacy, and the limitations of AI systems in processing extensive context lengths in prompts. The presenter stresses the importance of coherent vector space construction for accurate data retrieval and cautions against over-reliance on vector stores due to potential privacy issues. The video concludes by summarizing the pros and cons of various methods for connecting AI systems to external data, providing a comprehensive guide for integrating fine-tuned AI with real-time external data sources.
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#aieducation
#dataretrieval
