2.1.3. RAG (Retrieval-Augmented Generation) Pattern

• Concept: Combine retrieval from your data with LLM generation
• Purpose: Ground AI responses in accurate, current information
• Benefit: Reduce hallucinations, enable domain-specific answers

Visual: RAG Architecture

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Supported File Types for "Using Your Data":

• ✅ TXT, MD, HTML, PDF
• ✅ Microsoft Word (.docx)
• ✅ PowerPoint (.pptx)
• ❌ ZIP, XML (not supported)

Key Trade-Offs:

• Retrieval Quality vs. Latency: More thorough retrieval improves accuracy but adds latency
• Context Window vs. Cost: Including more context improves answers but increases token costs