RAG Systems Explained: How AI Agents Access Your Data

What is RAG?

Retrieval-Augmented Generation (RAG) is the technology that allows AI agents to access and use your specific business data, not just general knowledge.

The Problem with Standard AI

Standard AI models (like ChatGPT) don't know anything about:

How RAG Solves This

RAG combines two powerful techniques:

### 1. Retrieval

The AI searches your knowledge base for relevant information based on the user's question.

### 2. Generation

The AI uses retrieved information to generate accurate, contextual responses.

Technical Architecture

User Question

↓

Vector Database Search

↓

Retrieved Documents

↓

AI Model + Context

↓

Accurate Response

Implementation at MAZE

Our RAG implementation includes:

1. Document Processing: We convert your PDFs, docs, and webpages into searchable chunks

2. Embeddings: Each chunk is converted into a vector representation

3. Vector Database: We store vectors in a high-performance database

4. Semantic Search: User questions trigger similarity searches

5. Context Injection: Retrieved content is added to the AI's prompt

6. Response Generation: AI generates answers with your data as context

Performance Metrics

Common Use Cases

Security Considerations

Conclusion

RAG is what makes AI agents truly useful for businesses. It's the bridge between generic AI and AI that knows your business inside and out.

Interested in implementing RAG for your business? Contact us for a consultation.