Overview
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances Large Language Models by retrieving relevant text snippets from external vector databases before generating a response, ensuring responses are accurate, grounded, and up-to-date.
FAQ
Frequently Asked Questions
What is RAG and why does my enterprise need it?
RAG allows LLMs to query your private documents in real-time, giving accurate, non-hallucinated answers backed by direct citations.
What vector databases do you support?
We work with Pinecone, Qdrant, Milvus, Weaviate, and PostgreSQL with the pgvector extension.
Can RAG parse complex PDF tables and diagrams?
Yes. We build advanced multi-modal chunking pipelines using Vision models to parse tables, charts, and diagrams cleanly.
How do you handle document permission security in RAG?
We implement Metadata Filtering and ACL-aware retrieval so users only get answers from documents they have permissions to view.
How does RAG compare to fine-tuning an LLM?
Fine-tuning teaches an LLM style or domain tone, but is expensive and static. RAG provides real-time, easily updated knowledge retrieval at a fraction of the cost.
What document formats can be ingested?
PDFs, Word docs, Excel files, PowerPoint, HTML, Markdown, Notion pages, Confluence, and SQL databases.
How fast are RAG query responses?
With optimized vector indexes and hybrid search, retrieval takes under 200 milliseconds.
Can RAG be deployed on private on-premise servers?
Yes! We can deploy fully private RAG pipelines using local embedding models and pgvector/Qdrant in your local data center.
How do you evaluate RAG answer quality?
We use automated RAG evaluation frameworks (Ragas, TruLens) to measure context recall, context precision, and answer faithfulness.
How do we get started with RAG development?
Contact us for a technical demo and document audit to outline your custom RAG implementation roadmap.