想理解RAG和AI智能体从2023到2026的完整进化路径？这份116页幻灯片是绝佳教材，做检索增强生成或智能体开发的团队值得收藏。
LlamaIndex创始人Jerry Liu分享了@hexapode在新加坡AI工程师大会上的90分钟工作坊内容，包含116页幻灯片，系统梳理了RAG、检索、智能体循环、文档理解等AI模式在过去3年的演变。内容涵盖朴素RAG的12个痛点、重排序与查询重写的重要性、智能体循环如何简化检索层、文档解析的持续挑战，以及现代智能体形态如工作流和深度研究。对于关注AI技术演进的开发者，这是一份宝贵的历史脉络和实战经验总结。
A full tour through RAG, document context, and AI agents - from 2023 to 2026 🌎🤖 @hexapode gave a ...
A full tour through RAG, document context, and AI agents - from 2023 to 2026 🌎🤖 @hexapode gave a comprehensive 90-min workshop at @aiDotEngineer Singapore last week that comprehensively traces through how topics like retrieval, agent loops, agentic workflows, and document understanding have evolved in the last 3 years. We’re excited to share the 116-page slide deck online. If you’re seeing this for the first time, you’ll get a sense of how all AI patterns have evolved since the very beginning. Including the following topics: 💡 The 12 pain points of naive RAG 💡The importance of reranking and query-rewriting 💡How we’ve increased offloaded logic to the agentic loop as models improved (and coincidentally, the retrieval layer can get simpler) 💡Retrieval being the bottleneck as agents improved 💡Why document parsing is an extremely hard problem, even now in 2026 💡Exploring parsing outputs, from markdown to chunks to structured JSON metadata 💡Modern agent form factors around workflows and deep research If you’ve followed us or the space since the beginning, some of this will feel a bit nostalgic and will provide context on why our core focus today is narrowly focused on SOTA document parsing for agents. If you’re seeing this for the first time, hopefully there’s some useful historical context in here! drive.google.com/file/d/1IQ7G0a… y15yuJ2AW 💬 3 🔄 3 ❤️ 8 👀 462 📊 6 ⚡