RAG

What is it

RAG, or Retrieval-Augmented Generation, is an AI system architecture that solves the main problem of language models (LLMs): the fact that they have static knowledge, limited to the date they were trained and without access to a company's private data. Instead of relying solely on what the AI "memorized," RAG works like an open-book exam. When we ask a question, the system first searches the company's files (PDFs, databases, manuals) for the most relevant pieces of information and delivers them to the model along with the original question. Thus, the AI responds based on real and verifiable facts, and not just on statistical probabilities.

How it works

The RAG process is typically divided into four main phases that occur in milliseconds. First, there is the Ingestion phase: company documents are split into small blocks (chunking) and converted into mathematical representations called embeddings. These are stored in a vector database.

When a user asks a question, we enter the Retrieval phase: the system searches the database for the text blocks that have the highest semantic affinity with the query. It is not a search for exact keywords, but rather for meaning. In the third phase, Context, the most relevant results are attached to the instruction (prompt) that will be sent to the AI. Finally, Generation occurs: the model reads the question and the provided context to draft a coherent response, citing the sources consulted. It is this combination that allows an AI to "know" what happened in your warehouse yesterday afternoon or what the specific conditions of a customer's contract are.

When to use

RAG is the ideal path for almost all generative AI applications in a business context. It should be used when factual accuracy is critical and when information changes frequently. For example, if you need a customer support chatbot that knows current stock, or an internal tool that analyzes old commercial proposals to help create new ones.

It is preferable to Fine-tuning (retraining the model) in most business cases because it is cheaper, faster to implement, and allows the AI's knowledge to be updated simply by adding or removing a file from the source folder, without the need for complex technical training processes. Furthermore, RAG allows for maintaining privacy: you can define that certain documents are only consulted by users with specific permissions.

Common errors

One of the most frequent errors is providing too much or poor-quality context. If the system retrieves ten irrelevant documents, the AI will become confused and may ignore the correct information — this is the "context noise" phenomenon. Another critical error is neglecting the segmentation (chunking) phase; if text blocks are cut in the middle of an important sentence, the meaning is lost.

Many SMEs also forget the importance of data security. Putting procedure manuals into a RAG system connected to a public model without proper privacy layers can expose sensitive data. Finally, there is a tendency to ignore the "Reranker," an extra step that reorders the search results to ensure that what is most important actually appears at the top of the AI's reading hierarchy.

Practical example for an SME

Imagine a Portuguese SME that manufactures office furniture components and exports throughout Europe. The company has thousands of PDF technical data sheets, EU certification regulations, and a history of email exchanges with personalized technical specifications.

Without RAG, if the sales team asked the AI "Can we use component X in a project for a school in Germany?", the AI could invent an answer based on generic standards. With RAG, the system instantly searches the German regulations and the data sheets stored on the company's server. In seconds, it responds: "Yes, component X complies with the DIN 4550 standard required for school furniture, according to the safety certificate issued in March 2023 located in your Quality folder."

Frequently asked questions

Q: Is RAG more expensive than just using ChatGPT?
A: Operational costs may be slightly higher due to the vector database, but the return on investment is much higher as it avoids costly errors and eliminates the need for humans to search through extensive manuals.

Q: Do I need to know how to code to update the RAG's knowledge?
A: Not necessarily. In well-implemented systems, you just need to upload new files (PDF, Excel, Docx) to a folder or database and the system automatically processes them to be used in subsequent answers.

Q: How do I ensure that the AI doesn't invent answers with RAG?
A: We configure the system with strict instructions: "Answer only based on the provided context. If you don't find the information, say you don't know." This drastically reduces hallucinations.

Q: Can I use RAG with data that is in my ERP or CRM?
A: Yes. RAG is not limited to documents; it can be connected to structured databases so that the AI can answer about billing volumes, delivery times, or the purchase history of specific customers.

• 01Automatic analysis of contracts comparing new clauses with the company's legal history.
• 02Technical support chatbot that consults specific repair manuals for each machine model.
• 03Proposal assistant that extracts prices and conditions from internal Excel files to respond to tenders.
• 04Employee onboarding system that answers questions about the internal processes and culture manual.

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