RAG (Retrieval-Augmented Generation) applied to AI agents
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 informed 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.
Contact our sales team to activate Margot and her customizable AI agents.