# Retrieval Retrieval Augmented Generation (RAG): What It Is and How It’s Revolutionizing the Use of Artificial Intelligence in Business

What is Retrieval Augmented Generation (RAG)

RAG is a methodology that allows a language model (such as ChatGPT, Gemini, or Claude) to access external and specific information, for example company documents, internal policies, or technical manuals, in order to generate more accurate, coherent, and contextual responses.

Unlike a traditional LLM, which relies only on general knowledge learned during training, a RAG system:

1. Receives the user’s prompt.
2. Performs a semantic search within the provided company documents.
3. Retrieves the most relevant content.
4. Integrates that information into the context of the conversation.
5. Finally generates a response based on verifiable and up-to-date data.

In this way, responses do not come only from statistical correlations but from real sources made available by the organization.

The key role of vector databases

The technological core of RAG is the vector database, a system that stores and searches for information not by keywords but by meaning.

Tools such as Qdrant and Pinecone make it possible to store documents as embeddings, that is, numerical representations of their content. When the AI receives a question, it does not look for literal matches but for semantic ones. For example, it recognizes that “purchased” and “bought” have the same meaning.

How data preparation works

1. Company documents are collected (PDF, Word, Markdown, TXT, etc.).
2. Texts are divided into chunks, meaning smaller blocks of content.
3. Each chunk is transformed into a numerical vector.
4. The vectors are saved in the database and made searchable by the AI model.

When a question arrives, the system retrieves the most relevant chunks and integrates them into the model’s context, which then generates the final response.

Practical use cases of RAG

Automated Customer Care

One of the main areas where RAG is applied is customer service. Imagine a company that produces window frames, and a customer reports a defect on an installed window.

If the customer uses a chatbot on the company’s website, the AI interprets the issue, consults the knowledge base (manuals, price lists, previous reports), and automatically creates a detailed ticket.

If the customer instead contacts an AI voice agent, it processes the request and opens the ticket autonomously, since it is trained on the same documentation.

The result is a faster and more precise assistance flow that reduces handling times and frees human operators from repetitive tasks.

Knowledge management and internal training

Another common use of RAG is internal knowledge support.

In a logistics company, for instance, operational rules vary depending on the warehouse or the destination country. Finding the right procedure among dozens of manuals can take time. A RAG-based chatbot allows employees to directly query the company’s documentation using natural language, obtaining immediate and consistent answers aligned with official policies.

The same technology is also useful for training and onboarding. New employees can ask the chatbot questions and receive responses based on training materials, reducing the need for supervision and improving staff productivity.

Why RAG is a game changer for companies

Integrating a RAG-based solution means transforming information management within a company:

• Responses become accurate and verifiable.
• Workflows become automated.
• Productivity increases thanks to immediate access to knowledge.

In a context where generative AI is increasingly widespread, RAG represents the bridge between the linguistic power of models and the reality of corporate data.