{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-28T06:20:51.496Z","headline":"AI 构建者基础：Token、上下文窗口与 RAG 入门指南","description":"Google AI 发布了一期面向开发者的视频，系统讲解 LLM 核心概念。视频解释了 token 作为模型输入输出基本单元的含义，介绍了检索增强生成（RAG）如何为模型提供训练数据之外的额外信息，并说明了上下文窗口（即模型可处理的 token 数量）近年来大幅增长的趋势。视频还指出，给大语言模型提供更多上下文通常能提升回答质量。","url":"https://www.aioga.com/news/cms3dqexi0av9ro3f4lfse3bi/","mainEntityOfPage":"https://www.aioga.com/news/cms3dqexi0av9ro3f4lfse3bi/","datePublished":"2026-07-24T19:16:22.000Z","dateModified":"2026-07-24T19:16:22.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://dev.to/googleai/ai-builder-essentials-tokens-context-windows-and-rag-101-12bn","https://aihot.virxact.com/items/cms3dqexi0av9ro3f4lfse3bi"],"canonicalUrl":"https://www.aioga.com/news/cms3dqexi0av9ro3f4lfse3bi/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：Google AI 发布了一期面向开发者的视频，系统讲解 LLM 核心概念。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","url":"https://www.aioga.com/news/cms3dqexi0av9ro3f4lfse3bi/","dateCreated":"2026-07-24T19:16:22.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"dev.to source article","url":"https://dev.to/googleai/ai-builder-essentials-tokens-context-windows-and-rag-101-12bn","datePublished":"2026-07-24T19:16:22.000Z","provider":{"@type":"Organization","name":"dev.to","url":"https://dev.to/googleai/ai-builder-essentials-tokens-context-windows-and-rag-101-12bn"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cms3dqexi0av9ro3f4lfse3bi","datePublished":"2026-07-24T19:16:22.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cms3dqexi0av9ro3f4lfse3bi"}}],"aggregationSource":"Google AI：DEV 作者专属（RSS）","originalPublisher":{"name":"dev.to","url":"https://dev.to/googleai/ai-builder-essentials-tokens-context-windows-and-rag-101-12bn"},"article":{"id":"cms3dqexi0av9ro3f4lfse3bi","slug":"cms3dqexi0av9ro3f4lfse3bi","url":"https://www.aioga.com/news/cms3dqexi0av9ro3f4lfse3bi/","title":"AI 构建者基础：Token、上下文窗口与 RAG 入门指南","title_en":"AI builder essentials： tokens， context windows and RAG 101","summary":"Google AI 发布了一期面向开发者的视频，系统讲解 LLM 核心概念。视频解释了 token 作为模型输入输出基本单元的含义，介绍了检索增强生成（RAG）如何为模型提供训练数据之外的额外信息，并说明了上下文窗口（即模型可处理的 token 数量）近年来大幅增长的趋势。视频还指出，给大语言模型提供更多上下文通常能提升回答质量。","source":"Google AI：DEV 作者专属（RSS）","sourceUrl":"https://dev.to/googleai/ai-builder-essentials-tokens-context-windows-and-rag-101-12bn","aiHotUrl":"https://aihot.virxact.com/items/cms3dqexi0av9ro3f4lfse3bi","publishedAt":"2026-07-24T19:16:22.000Z","category":"技巧观点","score":37,"selected":false,"articleBody":["Tokens are an important foundational concept that underlie how we interact with LLMs. @greggyb：https://dev.to/greggyb and myself recorded a quick video to demystify what a token actually is and unpack several other related concepts.","To paint a bigger picture: more context you give a large language model, the better your responses are likely to be. The design patterns for providing that context have evolved at a rapid pace, and are likely to continue doing so.","What AI terms should we demystify next? Let us know!","Templates let you quickly answer FAQs or store snippets for re-use.","Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink：#.","For further actions, you may consider blocking this person and/or reporting abuse：/report-abuse","Google AI Studio is the fastest way to start building with Gemini. Ready to build?","Thank you to our Diamond Sponsors for supporting the DEV Community","Google AI is the official AI Model and Platform Partner of DEV","Neon is the official database partner of DEV","Algolia is the official search partner of DEV","DEV Community：/ — A space to discuss and keep up software development and manage your software career","Built on Forem：https://www.forem.com — the open source：https://dev.to/t/opensource software that powers DEV：https://dev.to and other inclusive communities.","Made with love and Ruby on Rails：https://dev.to/t/rails. DEV Community © 2016 - 2026.","We're a place where coders share, stay up-to-date and grow their careers."],"articleImages":[{"sourceUrl":"https://media2.dev.to/dynamic/image/width=256,height=,fit=scale-down,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F8j7kvp660rqzt99zui8e.png","alt":"pic","afterParagraph":2,"url":"/media/articles/cms3dqexi0av9ro3f4lfse3bi/f75d1e7bc8b434f4.webp"},{"sourceUrl":"https://media2.dev.to/dynamic/image/width=880%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fxjlyhbdqehj3akhz166w.png","alt":"Google AI - Official AI Model and Platform Partner","afterParagraph":7,"url":"/media/articles/cms3dqexi0av9ro3f4lfse3bi/ae5df90e75372c5a.webp"},{"sourceUrl":"https://media2.dev.to/dynamic/image/width=880%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fbnl88cil6afxzmgwrgtt.png","alt":"Neon - Official Database Partner","afterParagraph":8,"url":"/media/articles/cms3dqexi0av9ro3f4lfse3bi/f27898579e48288d.webp"},{"sourceUrl":"https://media2.dev.to/dynamic/image/width=880%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fv30ephnolfvnlwgwm0yz.png","alt":"Algolia - Official Search Partner","afterParagraph":9,"url":"/media/articles/cms3dqexi0av9ro3f4lfse3bi/ec27790c060bcd12.webp"}],"mediaStatus":"ok","articleBodyZh":["令牌（Tokens）是一个重要的基础概念，它是我们与大型语言模型（LLM）交互的基础。@greggyb：https://dev.to/greggyb 和我自己录制了一个简短的视频，来揭开令牌的实际含义，并解析其他几个相关概念。","为了勾勒出更大的画面：你提供给大型语言模型的上下文越多，你得到的回答可能就越好。提供上下文的设计模式正在快速发展，并且很可能会继续发展。","接下来我们应该揭开哪些 AI 术语的神秘面纱？请告诉我们！","模板让你能够快速回答常见问题或保存片段以供重复使用。","你确定要隐藏此评论吗？它将在你的帖子中变为隐藏状态，但仍可通过评论的永久链接查看：#。","进一步操作，你可以考虑屏蔽此人和/或举报滥用行为：/report-abuse","Google AI Studio 是使用 Gemini 构建的最快方式。准备好构建了吗？","感谢我们的钻石赞助商对 DEV 社区的支持","Google AI 是 DEV 的官方 AI 模型和平台合作伙伴","Neon 是 DEV 的官方数据库合作伙伴","Algolia 是 DEV 的官方搜索合作伙伴","DEV 社区：/ — 一个讨论、了解软件开发并管理你的软件职业的空间","基于 Forem：https://www.forem.com 构建 — 开源软件：https://dev.to/t/opensource 支持 DEV：https://dev.to 及其他包容性社区。","由爱与 Ruby on Rails 制作：https://dev.to/t/rails。DEV 社区 © 2016 - 2026。","我们是一个程序员分享、保持最新并发展职业的平台。"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Aioga 编辑摘要：Google AI 发布了一期面向开发者的视频，系统讲解 LLM 核心概念。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","background":"背景分析：实践类内容的价值在于是否能被复现、是否有明确边界，以及它能否转化为稳定的开发或工作流方法。","viewpoint":"Aioga 判断：这条动态更适合作为行业观察信号，当前信息足以建立线索，但不足以推导长期结论。","implications":"影响分析：对相关团队而言，短期应先核对来源、可用范围和实际成本，再判断是否值得接入或跟进。","nextStep":"后续观察：继续观察示例是否可复现、工具版本变化、社区反馈和实际成本。","evidenceRefs":["title","summary","articleBody"],"confidence":"medium","status":"published","aiGenerated":false,"autoApproved":true,"generatedBy":"rule-safe-fallback","generatedAt":"2026-07-28T06:29:10.526Z","sourceHash":"9126b3e84a7747eb","validation":{"passed":true,"mode":"rule-safe-fallback","checks":["schema","length","source-attribution","no-html"]}},"tags":["技巧观点","Google AI：DEV 作者专属（RSS）"],"translations":{"zh-CN":{"title":"AI 构建者基础：Token、上下文窗口与 RAG 入门指南","summary":"Google AI 发布了一期面向开发者的视频，系统讲解 LLM 核心概念。视频解释了 token 作为模型输入输出基本单元的含义，介绍了检索增强生成（RAG）如何为模型提供训练数据之外的额外信息，并说明了上下文窗口（即模型可处理的 token 数量）近年来大幅增长的趋势。视频还指出，给大语言模型提供更多上下文通常能提升回答质量。","category":"技巧观点","source":"dev.to","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"AI 构建者基础：Token、上下文窗口与 RAG 入门指南 - Aioga AI资讯","description":"Google AI 发布了一期面向开发者的视频，系统讲解 LLM 核心概念。视频解释了 token 作为模型输入输出基本单元的含义，介绍了检索增强生成（RAG）如何为模型提供训练数据之外的额外信息，并说明了上下文窗口（即模型可处理的 token 数量）近年来大幅增长的趋势。视频还指出，给大语言模型提供更多上下文通常能提升回答质量。","url":"https://www.aioga.com/news/cms3dqexi0av9ro3f4lfse3bi/"},"en":{"title":"Foundations for AI Builders: An Introduction to Tokens, Context Windows, and RAG","summary":"Google AI released a video aimed at developers, systematically explaining the core concepts of LLMs. The video explains the meaning of tokens as the basic units of model input and output, introduces how Retrieval-Augmented Generation (RAG) provides additional information to the model beyond the training data, and illustrates the trend of a significant increase in the context window (i.e., the number of tokens the model can process) in recent years. The video also points out that providing more context to large language models usually improves the quality of their responses.","category":"Insights","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Foundations for AI Builders: An Introduction to Tokens, Context Windows, and RAG - Aioga AI News","description":"Google AI released a video aimed at developers, systematically explaining the core concepts of LLMs. The video explains the meaning of tokens as the basic units of model input and...","url":"https://www.aioga.com/en/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:42:43.259Z"},"ja":{"title":"AI構築者の基礎：トークン、コンテキストウィンドウとRAG入門ガイド","summary":"Google AIは開発者向けの動画を公開し、LLMの核心概念を体系的に解説しました。動画では、モデルの入力・出力の基本単位としてのトークンの意味を説明し、検索強化生成（RAG）がモデルに訓練データ以外の追加情報を提供する方法を紹介し、コンテキストウィンドウ（つまりモデルが処理できるトークンの数）が近年大幅に増加している傾向を示しました。動画はまた、大規模言語モデルにより多くのコンテキストを提供すると、回答の質が向上することが多いとも指摘しています。","category":"ヒントと視点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"AI構築者の基礎：トークン、コンテキストウィンドウとRAG入門ガイド - Aioga AIニュース","description":"Google AIは開発者向けの動画を公開し、LLMの核心概念を体系的に解説しました。動画では、モデルの入力・出力の基本単位としてのトークンの意味を説明し、検索強化生成（RAG）がモデルに訓練データ以外の追加情報を提供する方法を紹介し、コンテキストウィンドウ（つまりモデルが処理できるトークンの数）が近年大幅に増加している傾向を示しました。動画はまた、大規模言...","url":"https://www.aioga.com/ja/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:42:47.686Z"},"ko":{"title":"AI 구축자 기초: 토큰, 컨텍스트 창과 RAG 입문 가이드","summary":"Google AI는 개발자를 대상으로 한 동영상을 공개했으며, LLM 핵심 개념을 체계적으로 설명했습니다. 동영상에서는 토큰이 모델 입력과 출력의 기본 단위라는 의미를 설명하고, 검색 강화 생성(RAG)이 모델에 훈련 데이터 외 추가 정보를 제공하는 방법을 소개하며, 컨텍스트 창(즉 모델이 처리할 수 있는 토큰 수)이 최근 몇 년간 크게 증가하는 추세임을 보여주었습니다. 또한 동영상은 대형 언어 모델에 더 많은 컨텍스트를 제공하면 일반적으로 답변의 품질을 높일 수 있음을 지적했습니다.","category":"인사이트","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"AI 구축자 기초: 토큰, 컨텍스트 창과 RAG 입문 가이드 - Aioga AI 뉴스","description":"Google AI는 개발자를 대상으로 한 동영상을 공개했으며, LLM 핵심 개념을 체계적으로 설명했습니다. 동영상에서는 토큰이 모델 입력과 출력의 기본 단위라는 의미를 설명하고, 검색 강화 생성(RAG)이 모델에 훈련 데이터 외 추가 정보를 제공하는 방법을 소개하며, 컨텍스트 창(즉 모델이 처리할 수 있는 토큰 수)이 최...","url":"https://www.aioga.com/ko/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:43:29.921Z"},"es":{"title":"Fundamentos para constructores de IA: Token, ventana de contexto y guía introductoria de RAG","summary":"Google AI lanzó un video dirigido a desarrolladores, explicando sistemáticamente los conceptos centrales de LLM. El video explica el significado de los tokens como la unidad básica de entrada y salida del modelo, presenta cómo la generación aumentada por recuperación (RAG) puede proporcionar información adicional al modelo más allá de los datos de entrenamiento, y muestra la tendencia al aumento significativo del tamaño de la ventana de contexto (es decir, la cantidad de tokens que el modelo puede manejar) en los últimos años. El video también señala que proporcionar más contexto a los grandes modelos de lenguaje generalmente puede mejorar la calidad de las respuestas.","category":"Ideas","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Fundamentos para constructores de IA: Token, ventana de contexto y guía introductoria de RAG - Aioga Noticias de IA","description":"Google AI lanzó un video dirigido a desarrolladores, explicando sistemáticamente los conceptos centrales de LLM. El video explica el significado de los tokens como la unidad básica...","url":"https://www.aioga.com/es/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:43:30.757Z"},"fr":{"title":"Fondamentaux des créateurs d'IA : guide d’introduction aux tokens, à la fenêtre contextuelle et au RAG","summary":"Google AI a publié une vidéo destinée aux développeurs, expliquant systématiquement les concepts clés des LLM. La vidéo explique la signification des tokens en tant qu'unités de base pour l'entrée et la sortie du modèle, présente comment la génération augmentée par récupération (RAG) peut fournir au modèle des informations supplémentaires au-delà des données d'entraînement, et indique la tendance à une augmentation significative du nombre de tokens que les modèles peuvent traiter dans la fenêtre contextuelle au cours des dernières années. La vidéo souligne également que fournir plus de contexte aux grands modèles de langage améliore généralement la qualité des réponses.","category":"Analyses","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Fondamentaux des créateurs d'IA : guide d’introduction aux tokens, à la fenêtre contextuelle et au RAG - Aioga Actualités IA","description":"Google AI a publié une vidéo destinée aux développeurs, expliquant systématiquement les concepts clés des LLM. La vidéo explique la signification des tokens en tant qu'unités de ba...","url":"https://www.aioga.com/fr/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:44:15.645Z"},"de":{"title":"Grundlagen für AI-Entwickler: Einführung in Token, Kontextfenster und RAG","summary":"Google AI hat ein Video für Entwickler veröffentlicht, das die Kernkonzepte von LLM systematisch erklärt. Das Video erläutert die Bedeutung von Token als grundlegende Einheiten für Ein- und Ausgaben des Modells, stellt vor, wie Retrieval-Augmented Generation (RAG) dem Modell zusätzliche Informationen über die Trainingsdaten hinaus bereitstellt, und zeigt den Trend der in den letzten Jahren stark gewachsenen Kontextfenster (also die Anzahl der vom Modell verarbeitbaren Token) auf. Das Video weist außerdem darauf hin, dass das Bereitstellen von mehr Kontext für große Sprachmodelle in der Regel die Antwortqualität verbessert.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Grundlagen für AI-Entwickler: Einführung in Token, Kontextfenster und RAG - Aioga KI-News","description":"Google AI hat ein Video für Entwickler veröffentlicht, das die Kernkonzepte von LLM systematisch erklärt. Das Video erläutert die Bedeutung von Token als grundlegende Einheiten für...","url":"https://www.aioga.com/de/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:44:14.788Z"},"pt-BR":{"title":"Fundamentos para Criadores de IA: Token, Janela de Contexto e Guia de Introdução ao RAG","summary":"O Google AI lançou um vídeo voltado para desenvolvedores, explicando sistematicamente os conceitos centrais de LLM. O vídeo explica o significado de token como a unidade básica de entrada e saída do modelo, apresenta como a geração aprimorada por recuperação (RAG) fornece ao modelo informações adicionais além dos dados de treinamento e explica a tendência de crescimento significativo do contexto de janela (ou seja, o número de tokens que o modelo pode processar) nos últimos anos. O vídeo também aponta que fornecer mais contexto para os grandes modelos de linguagem geralmente pode melhorar a qualidade das respostas.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Fundamentos para Criadores de IA: Token, Janela de Contexto e Guia de Introdução ao RAG - Aioga Notícias de IA","description":"O Google AI lançou um vídeo voltado para desenvolvedores, explicando sistematicamente os conceitos centrais de LLM. O vídeo explica o significado de token como a unidade básica de...","url":"https://www.aioga.com/pt-BR/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:44:57.502Z"},"ru":{"title":"Основы создания ИИ: токены, контекстное окно и вводное руководство по RAG","summary":"Google AI выпустила видео для разработчиков, в котором систематически объясняются основные концепции LLM. В видео разъясняется значение токена как базовой единицы ввода и вывода модели, рассказывается, как Retrieval-Augmented Generation (RAG) предоставляет модели дополнительную информацию помимо обучающих данных, и указывается на тенденцию значительного роста окна контекста (т.е. количества токенов, которые модель способна обработать) за последние годы. Видео также отмечает, что предоставление большего контекста большой языковой модели обычно улучшает качество ответов.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Основы создания ИИ: токены, контекстное окно и вводное руководство по RAG - Aioga Новости ИИ","description":"Google AI выпустила видео для разработчиков, в котором систематически объясняются основные концепции LLM. В видео разъясняется значение токена как базовой единицы ввода и вывода мо...","url":"https://www.aioga.com/ru/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:45:02.980Z"},"ar":{"title":"أساسيات بناء الذكاء الاصطناعي: رموز، نافذة السياق ودليل مبسط إلى RAG","summary":"أصدرت Google AI فيديو موجهًا للمطورين يشرح مفاهيم LLM الأساسية. الفيديو قد شرح معنى أن تكون الرموز وحدة أساسية لإدخال وإخراج النموذج، وقدم شرحًا لكيفية توفير الاسترجاع المعزز بالتوليد (RAG) لمعلومات إضافية للنموذج بخلاف بيانات التدريب، ووضح اتجاه الزيادة الكبيرة في نافذة السياق (أي عدد الرموز التي يمكن للنموذج التعامل معها) في السنوات الأخيرة. وأشار الفيديو أيضًا إلى أن تقديم المزيد من السياق للنماذج اللغوية الكبيرة عادة ما يعزز جودة الإجابات.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"أساسيات بناء الذكاء الاصطناعي: رموز، نافذة السياق ودليل مبسط إلى RAG - Aioga أخبار الذكاء الاصطناعي","description":"أصدرت Google AI فيديو موجهًا للمطورين يشرح مفاهيم LLM الأساسية. الفيديو قد شرح معنى أن تكون الرموز وحدة أساسية لإدخال وإخراج النموذج، وقدم شرحًا لكيفية توفير الاسترجاع المعزز بالتو...","url":"https://www.aioga.com/ar/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:45:50.276Z"},"hi":{"title":"एआई निर्माता आधार: टोकन, संदर्भ विंडो और RAG परिचय गाइड","summary":"Google AI ने डेवलपर्स के लिए एक वीडियो जारी किया, जिसमें LLM के मूलभूत सिद्धांतों की प्रणालीगत व्याख्या की गई। वीडियो ने यह समझाया कि token मॉडल के इनपुट और आउटपुट की मूल इकाई के रूप में क्या अर्थ रखता है, यह介绍 कि RAG (रिट्रीवल-एन्हांस्ड जनरेशन) मॉडल को प्रशिक्षण डेटा के अलावा अतिरिक्त जानकारी कैसे प्रदान करता है, और यह कि संदर्भ विंडो (यानी मॉडल द्वारा संसाधित किए जाने वाले token की संख्या) में हाल के वर्षों में महत्वपूर्ण वृद्धि की प्रवृत्ति रही है। वीडियो ने यह भी बताया कि बड़े भाषा मॉडल को अधिक संदर्भ प्रदान करने से आमतौर पर उत्तर की गुणवत्ता में सुधार होता है।","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"एआई निर्माता आधार: टोकन, संदर्भ विंडो और RAG परिचय गाइड - Aioga AI समाचार","description":"Google AI ने डेवलपर्स के लिए एक वीडियो जारी किया, जिसमें LLM के मूलभूत सिद्धांतों की प्रणालीगत व्याख्या की गई। वीडियो ने यह समझाया कि token मॉडल के इनपुट और आउटपुट की मूल इकाई के र...","url":"https://www.aioga.com/hi/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:45:53.059Z"},"it":{"title":"Fondamenti per i costruttori di AI: Token, finestra di contesto e guida introduttiva a RAG","summary":"Google AI ha pubblicato un video per sviluppatori, spiegando sistematicamente i concetti fondamentali dei LLM. Il video spiega il significato del token come unità base di input e output del modello, introduce come la generazione potenziata dalla ricerca (RAG) fornisce al modello informazioni aggiuntive oltre ai dati di addestramento e illustra la tendenza di crescita significativa negli ultimi anni della finestra contestuale (cioè il numero di token che il modello può gestire). Il video sottolinea anche che fornire più contesto a un grande modello linguistico di solito può migliorare la qualità delle risposte.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Fondamenti per i costruttori di AI: Token, finestra di contesto e guida introduttiva a RAG - Aioga Notizie IA","description":"Google AI ha pubblicato un video per sviluppatori, spiegando sistematicamente i concetti fondamentali dei LLM. Il video spiega il significato del token come unità base di input e o...","url":"https://www.aioga.com/it/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:46:35.465Z"},"nl":{"title":"Basisprincipes van AI-bouwers: Token, contextvenster en een introductiegids voor RAG","summary":"Google AI heeft een video voor ontwikkelaars uitgebracht waarin de kernconcepten van LLM systematisch worden uitgelegd. De video legt uit wat token betekent als de basis eenheid van input en output voor het model, introduceert hoe retrieval-augmented generation (RAG) het model extra informatie kan bieden buiten de trainingsdata, en toont de recente trend van een sterke toename van het contextvenster (het aantal tokens dat het model kan verwerken). De video wijst ook erop dat het geven van meer context aan grote taalmodellen meestal de kwaliteit van de antwoorden kan verbeteren.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Basisprincipes van AI-bouwers: Token, contextvenster en een introductiegids voor RAG - Aioga AI-nieuws","description":"Google AI heeft een video voor ontwikkelaars uitgebracht waarin de kernconcepten van LLM systematisch worden uitgelegd. De video legt uit wat token betekent als de basis eenheid va...","url":"https://www.aioga.com/nl/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:46:35.976Z"},"tr":{"title":"Yapay Zeka Yapıcı Temelleri: Jeton, Bağlam Penceresi ve RAG Başlangıç Kılavuzu","summary":"Google AI, geliştiricilere yönelik bir video yayınladı ve LLM'nin temel kavramlarını sistematik bir şekilde anlattı. Video, token'ların modelin giriş ve çıkışındaki temel birim olarak ne anlama geldiğini açıkladı, retrieval-augmented generation (RAG) yönteminin modele eğitim verisi dışında ek bilgi sağlama şeklini tanıttı ve bağlam penceresi (yani modelin işleyebileceği token sayısı) sayısının son yıllarda büyük ölçüde arttığı trendini gösterdi. Video ayrıca, büyük dil modellerine daha fazla bağlam sağlanmasının genellikle yanıt kalitesini artırabileceğini belirtti.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Yapay Zeka Yapıcı Temelleri: Jeton, Bağlam Penceresi ve RAG Başlangıç Kılavuzu - Aioga AI Haberleri","description":"Google AI, geliştiricilere yönelik bir video yayınladı ve LLM'nin temel kavramlarını sistematik bir şekilde anlattı. Video, token'ların modelin giriş ve çıkışındaki temel birim ola...","url":"https://www.aioga.com/tr/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:47:22.078Z"},"vi":{"title":"Cơ bản về người xây dựng AI: Hướng dẫn nhập môn về Token, cửa sổ ngữ cảnh và RAG","summary":"Google AI đã phát hành một video dành cho các nhà phát triển, hệ thống giải thích các khái niệm cốt lõi của LLM. Video giải thích ý nghĩa của token như là đơn vị cơ bản đầu vào và đầu ra của mô hình, giới thiệu cách tạo sinh được tăng cường truy xuất (RAG) cung cấp cho mô hình thông tin bổ sung ngoài dữ liệu huấn luyện, và chỉ ra xu hướng tăng mạnh của cửa sổ ngữ cảnh (tức là số lượng token mà mô hình có thể xử lý) trong những năm gần đây. Video cũng chỉ ra rằng việc cung cấp nhiều ngữ cảnh hơn cho mô hình ngôn ngữ lớn thường có thể cải thiện chất lượng câu trả lời.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Cơ bản về người xây dựng AI: Hướng dẫn nhập môn về Token, cửa sổ ngữ cảnh và RAG - Tin tức AI Aioga","description":"Google AI đã phát hành một video dành cho các nhà phát triển, hệ thống giải thích các khái niệm cốt lõi của LLM. Video giải thích ý nghĩa của token như là đơn vị cơ bản đầu vào và...","url":"https://www.aioga.com/vi/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:47:19.645Z"},"id":{"title":"Dasar-Dasar Pembuat AI: Token, Jendela Konteks, dan Panduan Pemula RAG","summary":"Google AI merilis sebuah video untuk pengembang, yang secara sistematis menjelaskan konsep inti LLM. Video tersebut menjelaskan arti token sebagai unit dasar input dan output model, memperkenalkan bagaimana Retrieval-Augmented Generation (RAG) dapat memberikan informasi tambahan di luar data pelatihan kepada model, dan menunjukkan tren peningkatan signifikan ukuran jendela konteks (yaitu jumlah token yang dapat diproses model) dalam beberapa tahun terakhir. Video itu juga menunjukkan bahwa memberikan lebih banyak konteks kepada model bahasa besar biasanya dapat meningkatkan kualitas jawaban.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Dasar-Dasar Pembuat AI: Token, Jendela Konteks, dan Panduan Pemula RAG - Berita AI Aioga","description":"Google AI merilis sebuah video untuk pengembang, yang secara sistematis menjelaskan konsep inti LLM. Video tersebut menjelaskan arti token sebagai unit dasar input dan output model...","url":"https://www.aioga.com/id/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:47:59.486Z"},"th":{"title":"พื้นฐานของผู้สร้าง AI: โทเค็น, หน้าต่างบริบท และคู่มือเริ่มต้น RAG","summary":"Google AI ได้ปล่อยวีดีโอสำหรับนักพัฒนา ที่อธิบายแนวคิดหลักของ LLM อย่างเป็นระบบ วีดีโอได้อธิบายความหมายของ token ในฐานะหน่วยพื้นฐานของการป้อนข้อมูลและผลลัพธ์ของโมเดล แนะนำการสร้างข้อมูลเสริมจากการค้นหา (RAG) ว่าจะให้ข้อมูลเพิ่มเติมนอกเหนือจากข้อมูลฝึกอบรมกับโมเดลอย่างไร และอธิบายถึงแนวโน้มการเพิ่มขึ้นอย่างมากของหน้าต่างบริบท (จำนวน token ที่โมเดลสามารถจัดการได้) ในช่วงไม่กี่ปีที่ผ่านมา วีดีโอยังระบุว่า การให้บริบทมากขึ้นแก่โมเดลภาษาขนาดใหญ่ มักจะช่วยเพิ่มคุณภาพของคำตอบได้","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"พื้นฐานของผู้สร้าง AI: โทเค็น, หน้าต่างบริบท และคู่มือเริ่มต้น RAG - ข่าว AI Aioga","description":"Google AI ได้ปล่อยวีดีโอสำหรับนักพัฒนา ที่อธิบายแนวคิดหลักของ LLM อย่างเป็นระบบ วีดีโอได้อธิบายความหมายของ token ในฐานะหน่วยพื้นฐานของการป้อนข้อมูลและผลลัพธ์ของโมเดล แนะนำการสร้างข...","url":"https://www.aioga.com/th/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:48:09.635Z"},"pl":{"title":"Podstawy tworzenia AI: Tokeny, okno kontekstu i wprowadzenie do RAG","summary":"Google AI opublikowało wideo skierowane do deweloperów, które systematycznie wyjaśnia podstawowe koncepcje LLM. Wideo tłumaczy znaczenie tokenu jako podstawowej jednostki wejścia i wyjścia modelu, przedstawia, jak generowanie wspomagane przez wyszukiwanie (RAG) może dostarczać modelowi dodatkowe informacje poza danymi treningowymi, oraz wskazuje na duży wzrost liczby tokenów, które model może przetworzyć w ramach okna kontekstowego w ostatnich latach. Wideo zauważa również, że dostarczenie dużym modelom językowym większej ilości kontekstu zwykle poprawia jakość odpowiedzi.","category":"技巧观点","source":"Google AI：DEV 作者专属（RSS）","aggregationSource":"Google AI：DEV 作者专属（RSS）","pageTitle":"Podstawy tworzenia AI: Tokeny, okno kontekstu i wprowadzenie do RAG - Aioga Wiadomości AI","description":"Google AI opublikowało wideo skierowane do deweloperów, które systematycznie wyjaśnia podstawowe koncepcje LLM. Wideo tłumaczy znaczenie tokenu jako podstawowej jednostki wejścia i...","url":"https://www.aioga.com/pl/news/cms3dqexi0av9ro3f4lfse3bi/","contentTranslated":true,"sourceHash":"4fc6f43d174fe221","translatedAt":"2026-07-28T02:49:01.536Z"}}}}