Hands-On AI: RAG using LlamaIndex
Released: 8/2/2024
Duration: 6h 25m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 1.21 GB
Genre: eLearning | Language: English
This course offers a deep dive into the principles and applications of retrieval-augmented generation (RAG), with a focus on the innovative LlamaIndex system. Explore how RAG enhances machine learning models by integrating external knowledge sources for more informed and accurate outputs. Instructor Harpreet Sahota
covers the architecture of retrieval systems, the mechanics of indexing vast datasets, and the integration of LlamaIndex with AI models.
Gain an understanding of the theoretical underpinnings of RAG, practical skills in building and deploying LlamaIndex, and review a critical analysis of RAG applications in various industries. Topics range from the basics of data retrieval and indexing to advanced techniques in enhancing generative models with external data. By the end of the course, you'll be prepared to design, implement, and evaluate RAG systems, positioning them at the cutting edge of AI technology implementation.
covers the architecture of retrieval systems, the mechanics of indexing vast datasets, and the integration of LlamaIndex with AI models.
Gain an understanding of the theoretical underpinnings of RAG, practical skills in building and deploying LlamaIndex, and review a critical analysis of RAG applications in various industries. Topics range from the basics of data retrieval and indexing to advanced techniques in enhancing generative models with external data. By the end of the course, you'll be prepared to design, implement, and evaluate RAG systems, positioning them at the cutting edge of AI technology implementation.
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