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Artificial Intelligence

Definition:Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an AI pattern that retrieves facts from private document databases to inform LLM responses.

Detailed Technical Explanation

RAG converts internal documents into vector embeddings. When a user asks a question, the vector database finds relevant text chunks and injects them into the LLM prompt.

Business Perspective

RAG grounds AI responses in real company data, preventing hallucinations while keeping data private.

Technical Perspective

Requires document chunking, embedding generation, vector databases (pgvector), and prompt context injection.

Real-World Example

Querying an internal RAG knowledge base for exact policy details across 5,000 corporate PDFs.

Common Architectural Mistakes

  • Poor Document Chunking: Splitting text mid-sentence, destroying semantic context for vector search.

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Frequently Asked Questions

Why is RAG better than fine-tuning for dynamic data?

Because RAG vector databases can be updated in seconds when files change without expensive model retraining.

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