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

Check out my LinkedIn: https://www.linkedin.com/in/donwoodlock/

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

---

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 Twitter: https://twitter.com/InterSystems