What is RAG?

Retrieval-Augmented Generation (RAG) — A powerful approach to AI that combines real-time data retrieval with language generation

Understanding RAG

Retrieval-Augmented Generation (RAG) is a technique that enhances AI language models by allowing them to access and use information from external knowledge sources in real-time, rather than relying solely on what they learned during training.

Think of it as giving an AI assistant access to a constantly-updated library. Instead of memorizing facts (which can become outdated), the AI can look up current information whenever it needs to answer a question.

How RAG Works

1.Index Your Data

Your content (documents, databases, websites, FAQs) is converted into searchable vectors and stored in a vector database. This creates a searchable knowledge base that the AI can query.

2.User Asks a Question

When a user asks a question, the system searches your indexed data to find the most relevant information related to that query.

3.Retrieve Relevant Context

The system retrieves the most relevant pieces of information from your knowledge base and provides them as context to the AI model.

4.Generate Response

The AI model uses both its training knowledge and the retrieved context to generate an accurate, up-to-date answer that's grounded in your actual data.

Why RAG Matters for Vertical AI

Always Up-to-Date

Your AI assistant can access the latest information from your database, website, or documents without needing to retrain the model. Update your content, and the AI immediately knows about it.

Domain-Specific Knowledge

RAG allows you to give the AI access to your specific business knowledge—product catalogs, pricing, policies, procedures—without needing to train a custom model from scratch.

Reduced Hallucinations

By grounding responses in retrieved documents, RAG helps prevent the AI from making up information. It can cite sources and say "I don't know" when information isn't available.

Cost-Effective

You can deploy a powerful AI assistant immediately using RAG, without the time and cost of collecting large training datasets or fine-tuning models. Start with RAG, then fine-tune later if needed.

RAG in Practice

Example: Bakery Assistant

A customer asks: "Do you ship conchas to New York?"

  1. 1. The system searches your shipping policy and product database
  2. 2. It retrieves relevant information about shipping zones and product availability
  3. 3. The AI generates a response: "Yes, we ship conchas to New York. Shipping typically takes 2-3 business days. Would you like to place an order?"

Example: Service Business

A customer asks: "What services do you offer for HVAC?"

  1. 1. The system searches your services database
  2. 2. It retrieves your HVAC service offerings, pricing, and availability
  3. 3. The AI generates a comprehensive response listing all HVAC services with current information

RAG vs. Fine-Tuning: When to Use Each

Use RAG For:

  • • Facts that change frequently (prices, inventory, hours)
  • • Large knowledge bases (product catalogs, documentation)
  • • Information that needs instant updates
  • • Getting started quickly without training data
  • • Domain-specific knowledge from your business

Use Fine-Tuning For:

  • • Conversation style and tone
  • • When to ask clarifying questions
  • • How to use tools and APIs
  • • Response structure and formatting
  • • Behavior patterns (after collecting real data)

Best Practice: Start with RAG to get a working system immediately, then fine-tune for behavior and style once you have real usage data. RAG and fine-tuning work best together—RAG handles facts, fine-tuning handles behavior.

Learn More

Want to see how we use RAG in practice? Check out our Vertical AI Development Roadmap to see how RAG fits into our complete methodology for building industry-specific AI systems.

View Our Roadmap →