Retrieval-Augmented Generation

A technique where an AI model retrieves relevant documents from your knowledge base before generating an answer, so the response is grounded in your actual content instead of the model's training data.

Technical definition

RAG combines a retrieval system (typically vector similarity search over document embeddings) with a generative language model. The retriever finds the top-k most relevant document chunks for a given query, and the generator produces a response conditioned on both the query and the retrieved context. This grounds the LLM's output in verified source material.

How Verabase uses RAG

Verabase uses a hybrid RAG pipeline that combines vector similarity search with full-text keyword matching. When a user asks a question, the system retrieves the most relevant chunks from your indexed knowledge base — including text extracted from PDFs, images, and charts via visual grounding — and feeds them to your chosen LLM. The AI Critic then verifies the response against the same source documents before it reaches the user.

Why it matters for support teams

Without RAG, AI chatbots answer from their general training data, which may be outdated, inaccurate, or completely fabricated. RAG ensures every answer comes from your verified documentation. For support teams, this means fewer hallucinations, higher customer trust, and lower ticket escalation rates.

Related terms

See also

How Verabase Handles Self-Healing Knowledge

The standard industry approach relies on support agents manually flagging stale content and technical writers updating documentation weeks later. Verabase completely upends this model through an autonomous AI platform that identifies knowledge gaps the moment an AI agent encounters a question it cannot answer.

Rather than requiring manual intervention, our visual RAG engine drafts a proposed fix. It analyzes the context of the user's question, reviews your existing knowledge base for conflicts, and creates a clear, structured article or snippet. All you need to do is click 'Approve'. This ensures your AI agents continually get smarter over time without adding to your support team's workload.

Security First Architecture

Many legacy platforms bolt AI onto their existing infrastructure, creating potential data leaks between tenants or exposing sensitive internal documents to end-users. Verabase is built from the ground up with a Bring Your Own Key (BYOK) architecture, enterprise-grade access controls, and strict semantic boundaries. This means your private engineering documents never accidentally leak into customer-facing agent responses.