--- title: "RAG (Retrieval-Augmented Generation) applied to AI agents | Margot.ai" description: "Retrieval-Augmented Generation, known as RAG, is a methodology in the field of artificial intelligence designed to enhance the performance of large language models (LLMs)" author: "user" date: "2025-09-23T13:09:45+00:00" language: "en-GB" canonical_url: "https://next.margot-ai.com/en/retrieval-augmented-generation-ai-agents" source_url: "https://next.margot-ai.com/en/retrieval-augmented-generation-ai-agents/" content_type: "text/markdown" --- [ ](//www.facebook.com/sharer/sharer.php?u=https://next.margot-ai.com/en/retrieval-augmented-generation-ai-agents) [](//x.com/intent/post?url=https://next.margot-ai.com/en/retrieval-augmented-generation-ai-agents) [](https://pinterest.com/pin/create/link/?url=https://next.margot-ai.com/en/retrieval-augmented-generation-ai-agents) [](https://www.linkedin.com/shareArticle?mini=true&url=https://next.margot-ai.com/en/retrieval-augmented-generation-ai-agents) # RAG (Retrieval-Augmented Generation) applied to AI agents ![RAG (Retrieval-Augmented Generation) applicata agli agenti AI](https://next.margot-ai.com/wp-content/uploads/2025/09/Rag_Margot.webp) **INDEX** - [What does RAG (Retrieval-Augmented Generation) mean and how does it change AI? ](#agentiai) - [The main applications and benefits of RAG ](#perch%C3%A8) - [Why choose RAG over a traditional LLM?](#come) - [The role of vector Databases in Retrieval-Augmented Generation ](#database) - [Margot: the AI Agent enhanced by RAG ](#margot) ## What does RAG (Retrieval-Augmented Generation) mean and how does it change AI? **Retrieval-Augmented Generation**, known as **RAG**, is a methodology in the field of **artificial intelligence** designed to enhance the performance of **large language models (LLMs)**. When a RAG system integrated into an **AI agent** generates a response, it does not rely solely on its internal knowledge: it accesses databases or specific documents selected by the **AI agent’s developers**. This approach combines the ability to retrieve relevant information with the capability **to generate coherent text**, improving both the **accuracy and the relevance of the responses** produced by the AI agent. ### The main applications and benefits of RAG **Retrieval-Augmented Generation (RAG)** is increasingly being **applied in AI agents** due to its ability to integrate up-to-date and specific knowledge directly into decision-making processes. This technology supports: - **Information Generation and Access** **RAG** facilitates the **rapid extraction** of relevant data from company documents, reports, and other internal sources, improving the **understanding of strategic information** and speeding up access to the content needed for i**nformed decision-making**. - **Customer Support** RAG enables the development of intelligent chatbots capable of providing precise and contextualized responses. By drawing on manuals, FAQs and company documentation, it **reduces problem-resolution times**, increases support efficiency, and ensures relevant, personalized answers. - **Internal Research Support** Companies can use RAG to help employees quickly **find specific information** within their digital archives, reducing the time spent on manual searches and **boosting productivity**. RAG can also be used to create **training support systems**, delivering accurate and contextual responses to new or **updating employees** on company procedures, internal policies and digital tools. ### Why choose RAG over a traditional LLM? Thanks to** RAG**, AI agents can fully harness the **power of LLMs**, enriching their responses with data from company documents, internal files, or web pages. This enables AI agents to deliver **more reliable, up-to-date and personalized results**, tailored to the specific needs of each application. Unlike traditional models, which generate responses based solely** on training data**, RAG allows AI agents to integrate information from** trusted external sources**, producing more accurate and **contextualized answers**. Adopting **Retrieval-Augmented Generation (RAG)** offers numerous benefits compared to conventional **large language models (LLMs)**, including: - **Greater Accuracy:** RAG uses reliable and verifiable sources, reducing the risk of incorrect information and increasing the trustworthiness of responses. - **Always Updated Information**: It allows the integration of recent and editable data, such as research or statistics, ensuring content is always relevant and current. - **Advanced Control for Developers:** Developers can customize responses by selecting and managing sources, ensuring consistency and relevance according to application goals. ### The role of vector Databases in Retrieval-Augmented Generation **Vector databases** are advanced systems that store numerical representations of data, called** embeddings**, capturing the semantic meaning of text, images, or other content. In the** context of RAG**,** AI agents** first convert documents into embeddings and store them in the database, which enables **fast similarity-based searches**. When a user asks a question, it is transformed into an embedding and compared with those in the database, allowing **the AI agent to retrieve relevant content** even without an exact word match. Thanks to semantic search, **vector databases** make AI agents’ **responses more accurate **and **contextually relevant**. ### Margot: the AI Agent enhanced by RAG **Margot** is an **AI agent** designed to optimize business processes through** intelligent automation**. It integrates **Retrieval-Augmented Generation (RAG)** technology to enhance the accuracy and relevance of its responses by accessing **up-to-date external knowledge sources**. This capability allows Margot to provide more precise **AI** **customer support** and enable **better-informed business decisions**, adapting to the specific **needs of each organization**. Would you like more information? Contact our sales team to activate Margot and her customizable AI agents. [ Request demo ](https://next.margot-ai.com/richiedi-demo) [ Contact us ](https://next.margot-ai.com/contatti) ##### [user](https://next.margot-ai.com/en/author/user)