什么是检索增强生成(RAG)？2026年AI大模型优化方案解析

生成式人工智能 (AI) 擅长根据其训练的大型语言模型 (LLM) 生成文本响应。这些模型在庞大数据集上进行训练，使其能够产生流畅、可读的文本，并广泛适用于回答各种问题或“提示”。

Generative Artificial Intelligence (AI) excels at producing text responses based on its training on Large Language Models (LLMs). These models are trained on vast datasets, enabling them to generate fluent, readable text that is broadly applicable for answering various questions or "prompts."

然而，其局限性在于，生成式 AI 的响应只能基于训练模型时所用的信息。对于通用 LLM 而言，这些数据可能是数周、数月甚至数年前的旧数据。因此，公司的 AI 聊天机器人可能无法获取关于其特定产品或服务的最新信息，从而导致回答不准确，并削弱客户和员工对技术的信任。

However, a key limitation is that a generative AI's responses are confined to the information used during its training. For a general-purpose LLM, this data can be weeks, months, or even years old. Consequently, a company's AI chatbot might lack up-to-date information about its specific products or services, leading to inaccurate answers that can erode trust among customers and employees.

RAG 如何解决 LLM 的局限性？

检索增强生成 (RAG) 正是为解决这一核心挑战而生。RAG 提供了一种优化 LLM 输出的方法，使其能够融入最新的、特定领域的信息，而无需重新训练底层模型。这意味着生成式 AI 系统可以提供更具情境相关性且基于当前数据的答案。

Retrieval-Augmented Generation (RAG) addresses this core challenge. It provides a method to optimize LLM outputs by incorporating up-to-date, domain-specific information without the need to retrain the underlying model. This enables generative AI systems to deliver more contextually relevant and current answers.

RAG 概念由 Patrick Lewis 和 Facebook AI Research 团队在 2020 年的一篇开创性论文中正式提出，并迅速获得了生成式 AI 开发社区的关注。如今，它被广泛认为是显著提升生成式 AI 系统实用价值的关键技术之一。

The RAG concept was formally introduced in a seminal 2020 paper by Patrick Lewis and the Facebook AI Research team, quickly gaining traction in the generative AI development community. It is now widely regarded as a pivotal technology for significantly enhancing the practical value of generative AI systems.

一个具体示例

以一个体育联盟为例，它希望球迷能通过在线聊天获取球员数据、比赛历史、规则以及实时统计和排名信息。通用 LLM 可以回答关于历史和规则的问题，但无法讨论昨晚的比赛或提供球员的最新伤情，因为它的知识是静态的，且重新训练的成本极高。

Consider a sports league that wants fans to access player data, game history, rules, and real-time stats via an online chat. A general-purpose LLM can answer questions about history and rules but cannot discuss last night's game or provide current injury updates, as its knowledge is static and prohibitively expensive to retrain.

而该联盟拥有或可以访问大量其他数据源：数据库、数据仓库、球员档案、赛事深度分析新闻流等。RAG 使生成式 AI 能够检索并利用这些信息，从而使聊天机器人能够提供更及时、更相关、更准确的响应。

The league, however, owns or has access to numerous other data sources: databases, data warehouses, player profiles, in-depth game analysis news feeds, etc. RAG enables the generative AI to retrieve and leverage this information, allowing the chatbot to deliver more timely, relevant, and accurate responses.

简而言之，RAG 帮助 LLM 提供更好的答案。

In short, RAG helps LLMs provide better answers.

核心要点

• RAG 是一种相对较新的人工智能技术，支持大型语言模型 (LLM) 无需重新培训即可利用更多的数据资源，从而提高生成式 AI 的质量。

  RAG is a relatively new AI technique that allows Large Language Models (LLMs) to utilize additional data sources without retraining, thereby improving the quality of generative AI.

• RAG 模型根据组织自身的数据构建知识库，并且可以不断更新存储库，帮助生成式 AI 提供及时、与具体情境相关的回答。

  RAG models build a knowledge base from an organization's own data, which can be continuously updated, helping generative AI provide timely and contextually relevant responses.

• 聊天机器人和使用自然语言处理的其他会话系统将从 RAG 和生成式 AI 中取得显著优势。

  Chatbots and other conversational systems using natural language processing stand to gain significant advantages from combining RAG with generative AI.

• 实施 RAG 需要像向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.这样的技术，从而快速完成新数据的编程工作，并根据该数据进行搜索并输入到 LLM 中。

  Implementing RAG requires technologies like vector databases to efficiently process new data, search based on it, and feed it into the LLM.

RAG 的工作原理

企业数据通常散落在各处：结构化数据库、非结构化文档（如 PDF）、博客、新闻源、客服聊天记录等。RAG 的工作流程始于将这些动态、异构的数据转换为统一格式，并存储在一个可供生成式 AI 系统访问的中央知识库中。

Enterprise data is often scattered: structured databases, unstructured documents (like PDFs), blogs, news feeds, customer service chat logs, etc. The RAG workflow begins by converting this dynamic, heterogeneous data into a unified format and storing it in a central knowledge base accessible to the generative AI system.

数据准备与检索

接下来，使用一种称为“嵌入模型”的特殊算法，将知识库中的内容处理成数字表示形式（即“向量”），并存储在高性能的向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.中。这种表示方式能够捕捉数据的语义含义，从而实现快速、精准的相似性搜索。

Next, a special algorithm called an "embedding model" processes the content in the knowledge base into numerical representations (called "vectors"), which are stored in a high-performance vector database. This representation captures the semantic meaning of the data, enabling fast and accurate similarity searches.

当用户提出一个问题（例如：“今晚比赛的地点、先发球员名单以及媒体前瞻是什么？”）时，该查询也会被转换成向量。系统随即在向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.中进行搜索，查找与查询向量最相似的、包含相关情境信息的文档片段。

When a user asks a question (e.g., "Where is tonight's game, who are the starting players, and what's the media preview?"), this query is also converted into a vector. The system then searches the vector database for document chunks whose vectors are most similar to the query vector, retrieving relevant contextual information.

生成增强的响应

检索到的情境信息与用户的原始提示一起被输入给 LLM。LLM 随后综合其固有的通用知识（来自原始训练）和刚刚提供的、最新的情境信息，生成最终的文字响应。

The retrieved contextual information, along with the user's original prompt, is fed into the LLM. The LLM then synthesizes its inherent general knowledge (from its original training) with the freshly provided, up-to-date contextual information to generate the final text response.

RAG 的关键优势：

• 持续更新：新数据可以持续、低成本地添加到知识库和向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.中，无需昂贵的模型重训练。

  Continuous Updates: New data can be added continuously and cost-effectively to the knowledge base and vector database, avoiding expensive model retraining.

• 可追溯性与可纠正性：由于系统知道答案来源于哪些具体文档，如果发现输出有误，可以追溯到源头文档进行更正或移除，从而提升后续回答的准确性。

  Traceability & Correctability: Because the system knows which specific documents contributed to the answer, inaccuracies can be traced back to source documents for correction or removal, improving future answer accuracy.

• 基于证据：RAG 为 LLM 的生成过程提供了外部证据支持，增强了答案的时效性、准确性和情境相关性。

  Evidence-Based: RAG provides external evidentiary support for the LLM's generation process, enhancing answer timeliness, accuracy, and contextual relevance.

RAG 与语义搜索基于语义理解而非关键词匹配的搜索技术，能理解查询意图和内容含义。

在提升基于 LLM 的 AI 准确性方面，语义搜索基于语义理解而非关键词匹配的搜索技术，能理解查询意图和内容含义。是另一项关键技术，它也是 RAG 的核心组成部分。

In the quest to improve the accuracy of LLM-based AI, semantic search is another key technology and forms a core component of RAG.

传统的关键字搜索匹配表面文字，可能遗漏语义相关但措辞不同的信息。语义搜索基于语义理解而非关键词匹配的搜索技术，能理解查询意图和内容含义。则通过理解查询和文档的深层含义来进行匹配，从而获得更准确、更全面的结果。RAG 利用语义搜索基于语义理解而非关键词匹配的搜索技术，能理解查询意图和内容含义。技术从知识库中检索最相关的情境信息。

Traditional keyword search matches surface text and may miss semantically relevant information phrased differently. Semantic search matches by understanding the deeper meaning of both the query and documents, leading to more accurate and comprehensive results. RAG leverages semantic search techniques to retrieve the most relevant contextual information from its knowledge base.

RAG 的优势与挑战

主要优势

• 信息时效性：可访问比 LLM 训练数据更新的信息。

  Information Freshness: Access to information newer than the LLM's training data.

• 成本效益：持续更新知识库的成本远低于频繁重新训练 LLM。

  Cost-Effectiveness: Continuously updating the knowledge base is far less expensive than frequent LLM retraining.

• 情境相关性：知识库可包含针对特定组织或行业的深度情境化数据。

  Contextual Relevance: The knowledge base can contain deeply contextualized data specific to an organization or industry.

• 来源可审计：信息来源可被追踪和验证，便于纠正错误。

  Source Auditability: Information sources can be traced and verified, facilitating error correction.

当前挑战

作为一项新兴技术，RAG 的实施也面临一些挑战：

• 认知与理解：提高企业对这项新技术的理解和认知。

  Awareness & Understanding: Increasing enterprise awareness and understanding of this relatively new technology.

• 初始成本与复杂度：虽然比持续重训练 LLM 便宜，但引入 RAG 系统（如向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.）会带来新的复杂性和成本。

  Initial Cost & Complexity: While cheaper than continual LLM retraining, introducing RAG systems (e.g., vector databases) adds new layers of complexity and cost.

• 数据建模：如何有效地对知识库中的结构化和非结构化数据进行建模和组织。

  Data Modeling: How to effectively model and organize both structured and unstructured data within the knowledge base.

• 流程制定：需要建立数据持续注入、错误信息识别与修正的标准化流程。

  Process Definition: Establishing standardized processes for continuous data ingestion, inaccuracy identification, and correction within the RAG system.

RAG 的应用与未来

典型用例

由 RAG 增强的生成式 AI 已应用于多个场景：

• 智能客服聊天机器人：提供基于最新产品信息、政策文档和交易记录的精准回答。

  Intelligent Customer Service Chatbots: Providing accurate answers based on the latest product information, policy documents, and transaction records.

• 企业知识问答：员工可查询内部技术文档、财务报告、会议纪要等。

  Enterprise Knowledge Q&A: Employees can query internal technical documentation, financial reports, meeting minutes, etc.

• 专业领域研究辅助：例如，在医疗数据库中快速查找相关研究论文，或在油气勘探数据中识别模式。

  Professional Domain Research Assistance: For example, quickly finding relevant research papers in medical databases or identifying patterns in oil and gas exploration data.

• 个性化推荐与摘要：分析用户个人数据（在合规前提下）或长文档，提供个性化摘要或建议。

  Personalized Recommendations & Summarization: Analyzing user personal data (under compliance) or long documents to provide personalized summaries or suggestions.

未来展望

当前，RAG 主要专注于提升问答的准确性。未来，其发展方向可能包括：

• 从“回答”到“行动”：RAG 增强的 AI 系统不仅能提供信息，还能根据情境和用户目标触发具体操作（例如，在查询度假信息后直接完成符合条件的房源预订）。

  From "Answering" to "Acting": RAG-augmented AI systems could not only provide information but also trigger specific actions based on context and user goals (e.g., booking a suitable rental after a vacation query).

• 处理复杂、多步骤任务：通过串联多次检索与生成步骤，协助用户完成复杂流程，如根据公司政策为员工推荐课程、协助报名并启动报销流程。

  Handling Complex, Multi-Step Tasks: By chaining multiple retrieval and generation steps, assisting users with complex workflows, such as recommending courses per company policy, assisting with enrollment, and initiating reimbursement.

结语

大型语言模型和生成式 AI 展现了巨大潜力。检索增强生成 (RAG) 通过为其注入时效性、准确性和深度情境化信息，正在成为释放这种潜力的关键技术。对于任何寻求将生成式 AI 应用于商业实践的组织而言，理解和探索 RAG 都至关重要。

Large Language Models and generative AI hold immense potential. Retrieval-Augmented Generation (RAG) is becoming a key technology to unlock this potential by infusing it with timeliness, accuracy, and deep contextualization. For any organization seeking to apply generative AI in business practice, understanding and exploring RAG is essential.

本文基于 Oracle 官方技术内容进行重构与升华，旨在提供更清晰、结构化的技术解读。

常见问题（FAQ）

RAG技术如何帮助AI聊天机器人回答最新信息？

RAG通过检索外部最新知识源（如数据库、新闻流）并整合到LLM中，使AI能基于实时数据生成答案，无需重新训练模型即可获取最新产品、服务或赛事信息。

RAG与普通语义搜索基于语义理解而非关键词匹配的搜索技术，能理解查询意图和内容含义。有什么区别？

RAG不仅检索相关信息，还将检索结果与LLM的生成能力结合，产生连贯、准确的回答；而语义搜索基于语义理解而非关键词匹配的搜索技术，能理解查询意图和内容含义。仅返回匹配的文档片段，缺乏上下文整合与自然语言生成。

企业实施RAG需要哪些关键技术？

需要向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.等技术，用于高效处理、存储和检索企业数据（如PDF、聊天记录），并将检索到的上下文输入LLM，以生成增强的响应。