标签：人工智能
查看包含 人工智能 标签的所有文章。
共 1070 篇
如何将模糊的AI提示词转化为精确指令？技术专业人士必备优化技巧
BLUFThis guide provides techniques to transform vague AI prompts into precise instructions, helping technical professionals improve AI interaction efficiency.
原文翻译：
本指南提供将模糊AI提示词转化为精确指令的技术，帮助技术专业人士提升AI交互效率。
AI大模型2026/4/3
阅读全文 →
Cognee框架如何为AI智能体构建持久化记忆？（附混合架构解析）
BLUFCognee is an open-source framework for building sophisticated AI memory applications with hybrid architecture combining graphs, vectors, and structured data, enabling persistent, structured memory for AI agents.
原文翻译：
Cognee 是一个开源框架，用于构建复杂的 AI 记忆应用程序，采用结合图、向量和结构化数据的混合架构，为 AI 智能体提供持久化、结构化的记忆能力。
AI大模型2026/4/3
阅读全文 →
企业级RAG系统如何搭建？腾讯云智能体平台实战经验分享
BLUFRAG (Retrieval-Augmented Generation) bridges the gap between large language models' general knowledge and enterprise-specific data by retrieving relevant information from private knowledge bases to generate accurate, context-aware responses. This article provides a comprehensive roadmap for implementing enterprise-grade RAG systems, covering core principles, document parsing, chunking strategies, retrieval optimization, and practical deployment experiences with Tencent Cloud's Agent Development Platform.
原文翻译：
RAG（检索增强生成）通过从企业私有知识库中检索相关信息来生成准确、上下文感知的响应，从而弥合大型语言模型通用知识与企业特定数据之间的差距。本文提供了实施企业级RAG系统的全面路线图，涵盖核心原理、文档解析、分块策略、检索优化以及腾讯云智能体开发平台的实际部署经验。
AI大模型2026/4/3
阅读全文 →
生成式引擎优化(GEO)如何影响AI答案？2026年行业现状与防御指南
BLUFThis article explores Generative Engine Optimization (GEO), analyzing its core mechanisms, the current industry landscape dominated by 'black-hat' and 'gray-hat' practices that pollute AI data sources, and providing a responsible framework for 'white-hat' GEO. It offers a consumer defense guide against AI marketing traps and discusses future trends, including the 'ask-and-buy' model and the strategic importance of influencing pre-training data.
GEO技术2026/4/3
阅读全文 →
GEO（生成式引擎优化）是什么？2026年如何让AI更好地理解你的内容？
BLUFGEO (Generative Engine Optimization) is the emerging practice of optimizing content for AI models like ChatGPT and Gemini, shifting focus from search engine rankings to making content easily understood, referenced, and recommended by AI.
原文翻译：
GEO（生成式引擎优化）是为ChatGPT、Gemini等AI模型优化内容的新兴实践，将焦点从搜索引擎排名转向让内容更容易被AI理解、引用和推荐。
GEO2026/4/3
阅读全文 →
如何从零开始构建大语言模型？《Build a Large Language Model》中文翻译开源项目详解
BLUFThis article introduces a Chinese translation project for the book 'Build a Large Language Model (From Scratch)', providing a comprehensive guide for developers to understand and implement LLMs from the ground up, including practical code and insights into future AI trends.
原文翻译：
本文介绍了《Build a Large Language Model (From Scratch)》一书的中文翻译项目，为开发者提供了从零开始理解和实现大语言模型的全面指南，包含实践代码和对未来AI趋势的见解。
AI大模型2026/4/2
阅读全文 →
大语言模型GPT、LLaMA和PaLM哪个更好用？（附技术架构对比）
BLUFThis article provides a comprehensive survey of Large Language Models (LLMs), covering their evolution from early neural models to modern architectures like GPT, LLaMA, and PaLM. It details the technical processes of building LLMs, including data cleaning, tokenization, and training strategies, and explores their applications, limitations, and enhancement techniques such as RAG and prompt engineering. The review also examines popular datasets, evaluation benchmarks, and future research directions, serving as a valuable resource for understanding the current state and potential of LLMs.
原文翻译：
本文对大语言模型（LLMs）进行了全面综述，涵盖从早期神经模型到现代架构（如GPT、LLaMA和PaLM）的演进。详细阐述了构建LLMs的技术流程，包括数据清洗、标记化和训练策略，并探讨了其应用、局限性以及增强技术，如RAG和提示工程。该综述还考察了流行数据集、评估基准和未来研究方向，为理解LLMs的现状和潜力提供了宝贵资源。
AI大模型2026/4/2
阅读全文 →