Large Model Technologies: RAG, AI Agents, Multimodal Applications, and Future Trends

The article introduces large models as the core engine of industry transformation, highlighting three key technologies—Retrieval‑Augmented Generation (RAG), AI agents, and multimodal models—that together address data timeliness, privacy, and specialized adaptation challenges.

RAG combines external knowledge retrieval with generative models to overcome static knowledge limits, improve answer reliability, and reduce inference costs by retrieving only relevant fragments. The text details the four‑step RAG pipeline (document ingestion, chunking, vectorization, and storage) and the difficulties of text chunk granularity, multimodal document handling, and controllable retrieval.

AI Agents are described as integrated systems that perceive environments, make autonomous decisions, and execute tasks. The article reviews open‑source frameworks such as MetaGPT and AutoGen, compares autonomous versus generative agents, and discusses multi‑agent collaboration for complex real‑world problems.

Multimodal Models are explored through three case studies: (1) Zhidong Taichu’s unified vision‑language model that merges detection, segmentation, and OCR tasks; (2) 360 Research Institute’s open‑world object detection for robust perception; and (3) Tencent’s multimodal video‑account review system that fuses visual and textual signals to automate content moderation.

The future outlook predicts a three‑spiral evolution: RAG advancing toward multimodal knowledge graphs, agents evolving into embodied intelligence, and multimodal models integrating neural‑symbolic reasoning, ultimately enabling end‑to‑end intelligent systems in fields such as robotics and smart grids.