What underlying technical support does RAG require?

RAG combines retrieval models with language generation models, requiring specific backend components to function effectively. Its implementation relies on three interconnected technical pillars: embedding models, vector databases, and integration pipelines alongside a large language model (LLM).

The core requirements include embedding models to convert text into numerical vectors capturing semantic meaning. A specialized vector database is essential for efficient storage, indexing, and similarity search across these embeddings. A capable LLM (e.g., GPT-4, LLaMA) is needed to process retrieved context and generate coherent responses. Middleware is also crucial for seamless orchestration between the retrieval and generation steps.

This underlying stack enables RAG's key application: grounding LLM responses in authoritative, specific data sources rather than static training knowledge. It enhances answer accuracy, reduces hallucinations, allows knowledge updates without full model retraining, and provides source citation. These capabilities deliver trustworthy AI responses in domains like customer support and enterprise knowledge bases.