System Design
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2026 Roadmap for AI Search & RAG Systems
Read Full Article: 2026 Roadmap for AI Search & RAG Systems
A practical roadmap for modern AI search and Retrieval-Augmented Generation (RAG) systems emphasizes the need for robust, real-world applications beyond basic vector databases and prompts. Key components include semantic and hybrid retrieval methods, explicit reranking layers, and advanced query understanding and intent recognition. The roadmap also highlights the importance of agentic RAG, which involves query decomposition and multi-hop processing, as well as maintaining data freshness and lifecycle management. Additionally, it addresses grounding and hallucination control, evaluation criteria beyond superficial correctness, and production concerns such as latency, cost, and access control. This roadmap is designed to be language-agnostic and focuses on system design rather than specific frameworks. Understanding these elements is crucial for developing effective and efficient AI search systems that meet real-world demands.
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From Object Detection to Video Intelligence
Read Full Article: From Object Detection to Video Intelligence
Object detection models like YOLO excel at real-time, frame-level inference and producing clean bounding box outputs, but they fall short when it comes to understanding video as data. The limitations arise in system design rather than model performance, as frame-level predictions do not naturally support temporal reasoning, nor do they provide a searchable or queryable representation. Additionally, audio, context, and higher-level semantics are often disconnected, highlighting the difference between identifying objects in a frame and understanding the events in a video. The focus needs to shift towards building pipelines that incorporate temporal aggregation, multimodal fusion, and systems that enhance rather than replace models. This approach aims to address the complexities of video analysis, emphasizing the need for both advanced models and robust systems. Understanding these limitations is crucial for developing comprehensive video intelligence solutions.