Retrieval-Augmented Generation (RAG) is a technique used to optimize the output of Large Language Models (LLMs) by referencing an authoritative knowledge base outside of the model's training data. Instead of relying solely on pre-trained internal knowledge, the AI retrieves relevant information from your company’s specific documents or databases before generating an answer. This significantly reduces hallucinations, improves accuracy, and ensures the model relies on your most current proprietary data without the high cost and complexity of fine-tuning or retraining.
Fine-tuning involves retraining a model to learn new patterns or specific tones, which is computationally expensive and static. RAG, specifically, focuses on retrieving new facts. It allows the model to look up information in real-time from your documents, making it cheaper to implement and easier to keep up-to-date.
It significantly reduces them, though it doesn't eliminate them entirely. By forcing the AI to "ground" its answers in specific facts retrieved from your reliable data sources, the model is much less likely to fabricate information compared to relying on its memory alone.
You can use almost any structured or unstructured data source relevant to your business, including:
Yes, but only if deployed correctly. If you use a public API for RAG, you risk exposing data. However, if you run the RAG pipeline inside your own secure infrastructure (like a private cloud VPC), your data remains governed by your internal security policies.
Shakudo allows you to deploy the entire RAG stack—including vector databases, embedding models, and LLMs—entirely inside your own infrastructure. This guarantees that the sensitive data being retrieved never leaves your governance boundary. We automate the orchestration of these tools, ensuring your RAG apps are secure, scalable, and audit-ready from day one.
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