# André Dias Moreira Prol: Securely Connect AI to Your Company Docs with RAG > Source: > Published: 2026-10-03 22:04:56+00:00 Imagine asking your AI assistant about last quarter's compliance report and getting a precise answer, sourced directly from your internal documents, without that data ever leaking to a public model. This is no longer science fiction—it's RAG (Retrieval-Augmented Generation) in production. Over my two decades in IT and Web3 infrastructure, I've rarely seen a technology deliver value as quickly as a well-architected RAG pipeline. Let me share how I approach building these systems securely. A common mistake I encounter when consulting with teams is the assumption that fine-tuning a model on company data is the best path. In practice, fine-tuning is expensive, slow to update, and—critically—bakes sensitive information directly into model weights, where it's nearly impossible to audit or delete. RAG takes a smarter route. Instead of retraining, it retrieves relevant document chunks at query time and injects them into the model's context window. The result: your proprietary data stays in a database you control, you can update knowledge instantly by re-indexing, and you maintain full traceability of every source. The numbers back this up. Studies from enterprise deployments show RAG can reduce hallucinations by 40–60% compared to base models, while costing a fraction of fine-tuning. For a mid-sized firm I advised last year, moving from a fine-tuning strategy to RAG cut their monthly AI infrastructure bill by nearly 70%. A production RAG system has four core stages. Here's the blueprint I recommend: ``` # Simplified RAG flow documents → chunking → embeddings → vector_store query → embedding → similarity_search → context → LLM → answer ``` **1. Ingestion & Chunking.** Split documents into semantically coherent pieces (300–500 tokens works well). Preserve metadata—document ID, access level, timestamp—for later filtering. **2. Embeddings & Vector Store.** Convert chunks into vectors using a model like `text-embedding-3-large` and store them in a vector database (Qdrant, pgvector, or Weaviate). For clients in regulated sectors, I always favor self-hosted options. **3. Retrieval.** When a query arrives, embed it and run a similarity search. I strongly recommend hybrid search—combining semantic vectors with keyword (BM25) matching—which in my experience improves retrieval accuracy by 15–25% on technical corpora. **4. Generation.** Pass the retrieved context plus the user question to the LLM, instructing it to answer *only* from provided sources and cite them. When I design these systems as André Dias Moreira Prol, I treat metadata as a first-class citizen. Without it, you can't enforce the access controls that make the difference between a useful tool and a data-breach liability. This is where my background in digital forensics shapes everything I build. RAG introduces real attack surfaces that are easy to overlook. A sobering statistic: a 2024 survey found that over 30% of organizations deploying generative AI had no access-control layer on their retrieval systems. That's a breach waiting to happen. The verifiability principles I apply from blockchain—immutable logs, cryptographic hashing of sources—translate surprisingly well to AI governance. RAG lets you unlock your organization's knowledge safely, but only when security is engineered in from the first line of code, not bolted on later. If you're ready to connect AI to your internal documents the right way, reach out—let's architect a system that's both powerful and provably secure. *Follow more articles by André Dias Moreira Prol on [Medium](https://medium.com/@andreprol).*