Ai+ Training

In this session, we explore the technical architecture behind creating a multimodal AI assistant using advanced Retrieval-Augmented Generation (RAG) techniques integrated with LlamaIndex for efficient data retrieval across diverse sources.

We will discuss how to address the limitations of native RAG models, including challenges with incomplete data, reasoning mismatches, and handling multimodal inputs like text, tables, and images. By leveraging LlamaIndex, along with visual language models and embedding techniques, we enable cross-modal understanding and more accurate information retrieval.

Attendees will learn how to utilize LlamaIndex for structuring and indexing large datasets, combine it with LangChain-based embeddings, and implement query decomposition and fusion strategies to enhance AI performance. This session is ideal for developers and data scientists looking to build robust AI systems capable of reasoning and retrieval across varied data types and formats.