Retrieval-Augmented Generation (RAG) enhances language models by grounding them in external data sources. Instead of generating from memory alone, a RAG model retrieves relevant information from structured databases, documents, or embeddings, then integrates that data into its response.  

This enables precision, prevents hallucination, and adapts the AI to your organization’s domain knowledge.

At Theta Tech, we design RAG systems that combine semantic and keyword search, hybrid ranking, and intelligent embeddings. Our architectures support PostgreSQL, open-source vector databases, or managed retrieval platforms.  

Each implementation is tailored to ensure relevance, performance, and interpretability.