What is RAG (Retrieval Augmented Generation)?
An AI architecture that improves large language model responses by first retrieving relevant information from a knowledge base, then using that context to generate more accurate, grounded answers.
Understanding the Details
RAG addresses a fundamental limitation of AI models: they only know what was in their training data. When you ask ChatGPT about your company's policies or your product's features, it's guessing. RAG fixes this by giving the AI access to your specific knowledge. When a question comes in, the system first searches your documentation to find relevant context, then passes that context to the AI along with the question. The AI generates an answer grounded in your actual content rather than generic knowledge. This enables accurate internal assistants, customer support bots, and knowledge tools.
How It Works in Practice
Internal knowledge assistant
Employees ask questions in Slack, and a RAG system searches company documentation to provide accurate answers with source citations.
Customer support bot
Support queries trigger retrieval from help articles and past tickets, enabling the AI to give specific, accurate responses.
Sales research tool
Before calls, sales reps ask an AI about prospects, and RAG retrieves relevant information from CRM, past conversations, and news sources.
Why It Matters
Generic AI isn't useful for company-specific questions. RAG lets you build AI tools that actually know about your business, enabling internal assistants, support automation, and knowledge systems that provide accurate, specific answers.
What People Often Get Wrong
RAG makes AI perfectly accurate. Actually, retrieval quality directly impacts answer quality, and AI can still make mistakes.
Any documents work for RAG. Actually, poorly structured or contradictory content creates poor retrieval results.
RAG replaces fine-tuning. Actually, they solve different problems and can be used together.
How We Handle RAG (Retrieval Augmented Generation)
We implement RAG systems with proper chunking, embedding strategies, and retrieval tuning. Our systems include source attribution so users can verify answers.
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Common Questions
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