How do you create an LLM that uses your own internal content?
You can imagine a patient visiting your website and asking a chatbot: “How do I prepare for my knee surgery?”
And instead of getting a generic answer from just ChatGPT, the patient receives an answer that retrieves information from your own internal documents.
The way you can do this is with a Retrieval Augmented Generation (RAG) architecture.
It’s not as complex as it sounds and I’m breaking down how this very popular solution works in today’s edition of #CodetoCare, my video series on AI & ML.
My next video will be on a use case of AI in healthcare – what do you want to hear about from me?
#AI #artificialintelligence #LLM #genai
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0:00 - 0:45 - Introduction: Guide to Retrieval-Augmented Generation
0:45 - 2:15 - Deep Dive: Understanding RAG in AI Systems
2:15 - 4:00 - Comparing Traditional Search with Language Learning Models
4:00 - 6:30 - Personalizing Content with RAG: Techniques and Benefits
6:30 - 8:00 - Scenario Analysis: Implementing RAG in Patient Chatbots
8:00 - 9:30 - Enhancing AI Prompts: Techniques for Improved Responses
9:30 - 11:00 - Content Segmentation: Preparing Data for RAG
11:00 - 11:36 - Conclusion: Summarizing the Advantages of RAG in AI
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ABOUT INTERSYSTEMS
Established in 1978, InterSystems Corporation is the leading provider of data technology for extremely critical data in healthcare, finance, and logistics. It’s cloud-first data platforms solve interoperability, speed, and scalability problems for large organizations around the globe.
InterSystems Corporation is ranked by Gartner, KLAs, Forrester and other industry analysts as the global leader in Data Access and Interoperability. InterSystems is the global market leader in Healthcare and Financial Services.
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