Intelligent RAG Systems That Know Your Data

RAG (Retrieval-Augmented Generation) grounds LLM responses in your actual data by retrieving relevant documents before generating an answer. The LLM can only cite and synthesize information it retrieves, dramatically reducing hallucinations compared to relying on the model's training data alone.

Most formats: PDFs, Word docs, HTML pages, Markdown, CSVs, JSON, emails, Slack messages, Confluence pages, Notion databases, SQL tables, and more. I build custom parsers for specialized formats and handle images/tables with multimodal approaches.

For most projects, Pinecone (managed, zero ops) or Qdrant (self-hosted, high performance) are excellent choices. If you already use PostgreSQL, pgvector avoids new infrastructure. For billion-scale datasets, Milvus handles distributed workloads. I'll recommend based on your scale and budget.

All data stays within your infrastructure or private cloud. I implement document-level access control, encryption at rest and in transit, and audit logging. For regulated industries, I deploy on-premise with air-gapped LLMs. No data ever leaves your security boundary.

RAG is best for factual Q&A over dynamic, up-to-date knowledge. Fine-tuning is better for teaching models a specific style, format, or domain reasoning. Often the best results come from combining both — fine-tune for style and domain understanding, then use RAG for grounded factual answers.