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RAG

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What is RAG?

Retrieval-Augmented Generation (RAG) is an AI framework that connects a large language model (LLM) to external knowledge sources at query time, grounding generated responses in real, retrievable documents rather than relying solely on the model's training data.

Introduced by Meta AI researchers in 2020, RAG addresses three core LLM limitations: knowledge cutoffs, lack of access to private organizational data, and hallucinations. Instead of retraining a model when information changes, a RAG system retrieves relevant content from internal documentation and passes it to the LLM as context — the mechanism behind AI agents that answer employee questions about IT procedures, HR policies, or benefits.

Key Takeaways

  • Retrieval-Then-Generation Pipeline: the system fetches relevant documents first, then generates a grounded response.
  • External Knowledge Connection: links an LLM to organizational data sources like wikis, knowledge bases, and HRIS systems.
  • No Model Retraining Required: knowledge updates happen in the document layer, not through costly model retraining.
  • Source-Attributable Responses: answers can cite the specific documents used to generate them.

Why RAG Matters

For IT and HR teams fielding repetitive questions across Slack threads and email, RAG turns static knowledge bases into active, queryable resources.

  • Reduced Ticket Volume: AI grounded in internal documentation can resolve common questions (VPN setup, leave policies) before they become tickets.
  • Accurate, Organization-Specific Answers: responses reflect your actual policies and procedures, not generic internet training data.
  • Lower Maintenance Costs: updating a knowledge article immediately improves AI responses, without retraining or redeployment.
  • Consistent Service Quality: every employee gets the same grounded answer regardless of who is on shift or how busy the team is.

RAG in Action

A three-person IT team at a 350-employee company is overwhelmed by repeated questions about password resets, Wi-Fi access, and VPN configuration. They connect their internal knowledge base to a RAG-powered AI assistant. When an employee asks "How do I connect to the VPN from my Mac?" in Slack, the system retrieves the relevant setup guide, generates step-by-step instructions specific to the company's configuration, and cites the source article. The IT team sees a measurable drop in routine tickets and reclaims time for infrastructure projects.

How Siit Supports RAG

Siit's Knowledge Agent uses retrieval-augmented generation to ground every response in your organization's actual documentation and operational data.

  • Knowledge Base Integrations: Siit connects to Notion and Confluence, pulling relevant articles into AI-generated responses so answers reflect your real policies and procedures.
  • AI Article Suggestion: during request submission, Siit analyzes the employee's question and surfaces matching knowledge base articles, deflecting routine inquiries before they reach an admin.
  • AI Triage With Context: requests that require human attention arrive pre-classified with relevant documentation attached, giving admins full context from the start.
  • 360° Employee Profile: Siit combines retrieved knowledge with employee-specific data from integrated HRIS, IAM, and MDM systems, so responses account for role, location, and permissions.

Because Siit pairs RAG-powered knowledge retrieval with AI-Powered Workflows and native integrations across Okta, Jamf, BambooHR, and 50+ other tools, answers can lead directly to automated actions rather than stopping at information.

Want to see how RAG resolves employee requests from your own knowledge base? Book a demo and see how Siit works.