Generative AI (GenAI) has become an increasingly vital tool for companies, revolutionizing how content is created and how we interact with technology, particularly through chatbots and virtual assistants. This technology is based on extremely large and complex Machine Learning linguistic models called Large Language Models (LLMs), which are capable of generating human-like content based on user requests.
Initially only able to provide simple text-to-text content, in recent years we have observed a extraordinary evolution of LLMs. They are now capable of writing articles, generating functional code, producing images, videos, and much more.
The slow adoption of Generative AI
Despite the capabilities of Large Language Models, they have certain limitations, and their adoption within corporate contexts is still in the early stages.
Ideally, businesses want versatile, fast models that are always up-to-date with the company’s information assets. Having a virtual assistant capable of connecting to corporate data leads to a sharp increase in productivity and lowers the barrier for adopting Data Analytics and Business Intelligence technologies, which are increasingly important for maintaining a competitive edge.
The main limitation of LLMs is that they only know the information they were trained on. These models are built using complex statistical algorithms that analyze the content of articles, books, lines of code, etc., provided to them. A model released in late 2024 cannot possess certain information about 2025 unless it undergoes a new training procedure.
The challenge of Hallucinations
Those who frequently use GenAI know that models are subject to “Hallucinations”: an anomalous behavior where a response is generated that is semantically correct and convincing, yet factually inaccurate. Returning to the example above, a model might generate a response regarding facts from 2025 using events from 2024 as sources, or it might cite articles that do not exist.
The high costs of LLMs
LLMs are extremely complex and expensive to train and maintain. Consequently, most users leverage them via an “as-a-service” paradigm, where they pay based on usage without having control over the entire infrastructure.
Expanding an LLM’s knowledge through retraining (technically known as “Fine-Tuning”) is generally not a viable path due to cost and complexity. In this context, RAG technology represents a valid alternative for adding relevant information to models without the need for training, allowing for increasingly accurate responses.
How does Retrieval-Augmented Generation (RAG) work?
The RAG architecture involves connecting the LLM to one or more document databases. When a user makes a request:
- The request is analyzed and used to extract all documents (or parts of documents) from the DBs that may be relevant to the answer.
- After a brief reprocessing, both the initial user request and the relevant documents are passed to the LLM.
- The LLM now has far more information available to satisfy the user’s request.
Effectively, adopting RAG does not require modifying the GenAI model itself and can be seen as an advanced version of Prompt Engineering: A process of analysis and information gathering is built around the model, allowing the delivery of a highly informative prompt (request + context). This significantly reduces the risk of LLM hallucinations regarding topics present in the document databases.
Implementing a RAG Architecture
The implementation of a RAG architecture can be divided into 3 phases:
- Data Preparation (Embedding and Vectorization): Document databases are created and populated. Document content is divided into blocks (“Chunks”) and then transformed from text/images into numerical vectors (“Embeddings”) for storage. This procedure is called Vectorization and can be performed using Deep Learning models.
- Setup and Retrieval: A process is built to receive the user’s request and retrieve the most pertinent data from the document database. All this data is reprocessed to generate a single prompt containing the user’s request and the context data.
- Generation (Augmented Generation): The Large Language Model receives the prompt constructed in the previous phase and generates an accurate, contextualized response. Since these models can accept considerable prompt sizes, adding as much context information as possible is often decisive in improving answers.
RAG-based AI Applications
By using RAG technology, it is possible to enhance an LLM’s capabilities by adding context from external sources: company documents, the internet, databases, historical tables, etc.
Examples of applications include:
Corporate Chatbots and Customer Support: RAG-based chatbots provide accurate, personalized answers based on internal data (e.g., manuals, reports, product sheets), improving customer support and internal efficiency.
Document Analysis and BI: RAG can automate data extraction from unstructured documents (e.g., invoices, contracts) and support Business Intelligence by providing analysis and forecasts based on real-time corporate data.
What are the Advantages of Retrieval-Augmented Generation?
Compared to the Fine-Tuning strategy, which requires retraining a model, the RAG strategy is much more economical and allows for responses that are always up-to-date, as the model remains agnostic to corporate information, which is passed with every prompt.
Building AI applications via RAG is also simpler, as they integrate perfectly with Generative AI as-a-service offerings. Cloud providers like AWS, Google, and Microsoft provide cloud services to build, deploy, and monitor RAG technologies.
From a security standpoint, a RAG application maintains data governance since data remains within the cloud or corporate infrastructure. Furthermore, if correctly implemented, it ensures compliance with GDPR (General Data Protection Regulation) because PII (Personally Identifiable Information) is not exposed to unauthorized users.
Contact the Experts
Blue BI considers innovation the engine of its growth, and the arrival of Generative AI combined with RAG technology represents a tool capable of redesigning processes, decisions, and customer interactions. Our solutions include always-updated chatbots and virtual assistants, analytics enriched by corporate information, and much more.
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