RAG-Driven Generative AI
Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone
Created by Denis Rothman
Unlock the power of retrieval augmented generation by building custom AI pipelines that combine large language models, computer vision, and advanced data retrieval. Learn how to create scalable, accurate, and cost-effective solutions using tools like LlamaIndex, Deep Lake, and Pinecone.
Packt | Sep 2024 | 338 min
What You Will Learn
You will start by exploring the fundamentals of retrieval augmented generation and quickly move into hands-on projects that use popular frameworks and vector databases. Through practical exercises, you will learn to connect data sources, manage indexing, and fine-tune AI models for real-world applications.
Key Features
- Design and implement RAG pipelines for reliable, traceable AI outputs
- Optimize generative AI performance using real-time feedback and knowledge graphs
- Balance cost and accuracy by managing dynamic retrieval and fine-tuning strategies
Target Audience
Perfect for data scientists, AI engineers, and technical professionals with some experience in machine learning. If you want to build or improve AI systems that deliver accurate, transparent results across different domains, this course will help you gain the skills needed to design and deploy advanced RAG-driven solutions.
