André Dias Moreira Prol: RAG conectando IA aos documentos da empresa

André Dias Moreira Prol explains how Retrieval-Augmented Generation (RAG) technology enables companies to utilize their internal knowledge while maintaining security and compliance. RAG enhances large language models by retrieving relevant document sections before generating responses. This technology reduces the time spent by legal teams locating specific clauses by up to 70%.

However, implementing RAG requires strict security measures to prevent confidential data leaks, as required by the General Data Protection Regulation (LGPD) in Brazil, which can result in fines of up to R$ 50 million for infractions. Key security practices include isolating the environment, using non-training clauses for external APIs, access control via metadata, pre-recognition filtering, PII masking, and maintaining an audit trail.

A recommended architecture for beginners includes using a vector database like pgvector on PostgreSQL, oration tools like LangChain or LlamaIndex, hosting open-source models for critical data, and utilizing commercial APIs for public data. Continuous evaluation of groundedness and relevance is crucial. A common mistake is treating RAG as a plug-and-play solution; 80% of the effort lies in the quality of data ingestion, including document structuring, intelligent chunking, and metadata enrichment.

Adjusting chunking strategies can improve response accuracy by over 40%. Implementing re-ranking by retrieving 20 chunks and having a secondary model reorder the 5 most relevant shows a significant improvement in user-perceived quality. Overall, RAG transforms neglected documents into a competitive advantage, provided security and governance are prioritized before speed.