What Is RAG?
Retrieval-Augmented Generation (RAG) is a powerful strategy that allows language models to generate more accurate, up-to-date, and relevant responses by referencing external information sources—especially useful in enterprise settings. In a traditional LLM scenario, the model generates answers solely based on what it learned during training. RAG changes this by combining the power of retrieval with generation. It works by:
- Retrieving relevant documents from a knowledge base (usually using embeddings and vector similarity search)
- Generating a response using an LLM that references the retrieved context
Why it matters for enterprises
- Keeps your knowledge base dynamic without retraining models
- Ensures responses are grounded in your actual data
- Enables secure, context-aware applications like internal assistants, FAQ bots, document summarizers, etc.
How RAG Edges Work in The Workflow?
- Enterprise documents (e.g., PDFs, FAQs, reports) are first uploaded or added to Structured Text Blocks.
- Content is split into chunks for better indexing and retrieval.
- The semantically meaningful content is embedded and stored in a local vector database.
- Later, a query triggers a retrieval process and the top results are passed into an LLM generator to produce an answer.