André Dias Moreira Prol: Securely Connect AI to Your Company Docs with RAG

The use of Retrieval-Augmented Generation (RAG) enables businesses to securely connect AI to their internal documents without risking leaks. RAG is a technology that has been proven to deliver value quickly, making it a notable advancement in the field. Over the course of his two-decade career in IT and Web3 infrastructure, the author has seldom encountered a technology as swiftly impactful as RAG when properly architected.

A common misconception among teams is that fine-tuning a model on company data is the optimal approach. However, fine-tuning is costly, slow to update, and potentially exposes sensitive information directly into model weights, making it challenging to audit or delete. RAG, on the other hand, retrieves relevant document chunks at query time and injects them into the model's context window, preserving data in a controllable database, allowing for instant updates by re-indexing, and maintaining full traceability of source data.

Enterprise studies demonstrate that RAG can significantly reduce hallucinations by 40-60% compared to base models, while costing less than fine-tuning. For a mid-sized firm the author advised, transitioning from fine-tuning to RAG led to a nearly 70% reduction in their monthly AI infrastructure bill.

The core stages of a production RAG system include ingestion and chunking, embeddings and vector store, retrieval, and generation. Documents are split into semantically coherent pieces, typically 300-500 tokens, preserving metadata such as document IDs, access levels, and timestamps for later filtering. The chunks are then converted into vectors using a model like text-embedding-3-large and stored in a vector database, with self-hosted options preferred for regulated sectors.

Upon receiving a query, the system embeds it, performs a similarity search, and passes the retrieved context plus the user's question to the LLM, instructing it to generate an answer based solely on the provided sources, citing them.

As André Dias Moreira Prol, the author emphasizes the importance of treating metadata as a first-class citizen, as it is crucial for enforcing access controls and preventing data-breach liability. Security considerations in RAG architecture are paramount, with access control at retrieval time, filtering vector searches by user permissions, prompt injection defense, data residency, encryption, and PII redaction at ingestion being key practices.

The author draws on his background in digital forensics to highlight the need for these controls, noting that many organizations overlook access control layers in their retrieval systems, leading to potential breaches.

In conclusion, RAG offers a secure and efficient way to leverage internal knowledge through AI, but security must be integrated from the beginning of the system's development.