Retrieval-Augmented Generation (RAG) with Embeddings & Vector Databases
In this course, you will explore advanced AI engineering concepts, focusing on the creation, use, and management of embeddings in vector databases, as well as their role in Retrieval-Augmented Generation (RAG).
You will start by learning what embeddings are and how they help AI interpret and retrieve information. Through hands-on exercises, you will set up environment variables, create embeddings, and integrate them into vector databases using tools like Supabase.
As you progress, you will take on challenges that involve pairing text with embeddings, managing semantic searches, and using similarity searches to query data. You will also apply RAG techniques to enhance AI models, dynamically retrieving relevant information to improve chatbot responses. By implementing these strategies, you will develop more accurate, context-aware conversational AI systems.
This course balances both the theory behind AI embeddings and RAG with practical, real-world applications. By the end, you will have built a proof of concept for an AI chatbot using RAG, preparing you for more advanced AI engineering tasks.
Status: Large Language Modeling
Large Language ModelingStatus: Semantic Web
Semantic WebIntermediate·Course·4 hours
