AI Free Course Online – Building Agentic RAG with LlamaIndex

Course: Building Agentic RAG with LlamaIndex
Instructor: Jerry Liu (Co-founder and CEO of LlamaIndex)
Cost: Completely Free 🎉🎉🎉
Difficulty: Intermediate 📚 📚(Python programming required)
Duration: Short ⏳ (1 Hour)
Certificate: Shareable Link 📜

Some of the shortcomings of standard RAG (Retrieval Augmented Generation) include:

• Retrieval Challenges: First, it might pick up irrelevant or poorly aligned chunks of text. Second, it might miss crucial information altogether. This can lead the LLM to base its response on inaccurate or incomplete data.
• Integration Issues: Even when the retrieved information is relevant, standard RAG might struggle to effectively combine it with the user prompt. The information might be disjointed or repetitive, making it difficult for the LLM to generate a coherent and informative response.

Agentic RAG aims to improve upon the standard RAG pipeline, utilizing powerful agents that act as research tools that can dig deeper and analyze data more comprehensively. In particular, Agentic RAG is good at navigating, summarizing, and comparing information across multiple documents, something that standard RAG often struggles with.

One of the best frameworks for RAG is LlamaIndex, thanks to their extensive libraries covering advanced RAG techniques. The free course below covers how to build AI models that can answer questions and summarize documents. There is a fully functioning code example on how to build a Multi-Document Agent, that can easily be adapted to one’s use case.