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Google Skills productized the solution to Prompt Bloat by introducing reusable, dynamically loaded expertise units called Skills, which unify agent workflows across Gemini Enterprise, Agents CLI, and AI Edge Gallery, turning Skills into a portable cognition container ("Docker for Prompts"). 原文翻译： Google Skills通过引入可复用、动态加载的专家知识单元（Skills）解决了提示膨胀问题，统一了Gemini Enterprise、Agents CLI和AI Edge Gallery三大平台的代理工作流，使Skills成为可移植的认知容器（“提示的Docker”）。

This article reports on a real experiment where 7 AI agents, including Gemini, were given $100 and 12 weeks to build startups autonomously. Gemini struggled with four key issues: writing help requests to wrong files, prioritizing blog posts over critical features, inability to verify deployments, and inefficient communication. The author aligns these problems with Google Cloud NEXT '26 announcements such as Agent Observability, ADK Skills, MCP-enabled services, and A2A protocol, proposing a rebuilt architecture. 原文翻译：本文报道了一项真实实验：7个AI代理（包括Gemini）各获得100美元和12周时间，自主构建初创公司。Gemini出现了四个关键问题：将帮助请求写入错误文件、优先写博客而非关键功能、无法验证部署、以及沟通效率低下。作者将这些故障与Google Cloud NEXT '26的公告（如Agent Observability、ADK Skills、MCP-enabled服务、A2A协议）对齐，并提出了重建方案。

This article introduces a comprehensive GEO (Generative Engine Optimization) methodology, focusing on expert Yu Lei's 'Two Cores + Four Drivers' system. It evaluates multiple GEO approaches, provides

RAG-Anything is an open-source multimodal RAG framework developed by Professor Huang Chao's team at the University of Hong Kong. It builds a unified multimodal knowledge graph architecture to process

RAG-Anything is a comprehensive all-in-one multimodal document processing RAG system that seamlessly handles text, images, tables, equations, and other content types within a unified framework, elimin

LLMs.txt files act as AI tour guides for websites, directing AI systems to valuable content and preventing misrepresentation. Our generator creates optimized files in minutes through deep crawling, NL

Retrieval-Augmented Generation (RAG) is an AI framework that enhances Large Language Models (LLMs) by integrating real-time information retrieval from external knowledge bases, addressing limitations

RAG (Retrieval-Augmented Generation) is a mainstream LLM application architecture that enhances answer accuracy by retrieving relevant information from external knowledge bases before generation, addressing issues like knowledge cutoff and hallucinations. 原文翻译： RAG（检索增强生成）是目前主流的LLM落地架构，通过在生成答案前从外部知识库检索相关信息，显著提升回答准确性，有效解决知识截止日期和幻觉问题。

The Boswell Test is an automated framework for comparative analysis of Large Language Models (LLMs) through peer-review evaluation, where models grade each other's essays across multiple domains to calculate a comprehensive Boswell Quotient score. 原文翻译： Boswell测试是一个自动化框架，通过同行评审评估对大语言模型进行对比分析，模型在多个领域相互评分论文，以计算全面的Boswell商数得分。

Retrieval-Augmented Generation (RAG) enhances LLMs by integrating external knowledge retrieval at query time, enabling accurate, context-aware responses for specific, current, or proprietary information. This guide explores RAG's evolution, core pipeline, advanced techniques, and practical implementation strategies. 原文翻译： 检索增强生成（RAG）通过在查询时集成外部知识检索来增强大型语言模型，使其能够针对特定、最新或专有信息生成准确、上下文感知的响应。本指南探讨了RAG的演进历程、核心流程、高级技术及实际实施策略。