GPT-RAG

GPT-RAG connects a Large Language Model (LLM) like GPT-4 to external, authoritative data sources, drastically reducing hallucinations and providing real-time, fact-based answers.

Retrieval-Augmented Generation (RAG) is the critical architecture for grounding Large Language Models in verifiable, enterprise-specific data. The process is efficient: a user query triggers an immediate semantic search (retrieval) across an external vector database (e.g., 10,000 internal documents), which returns the most relevant text chunks. These chunks are then dynamically injected into the prompt, augmenting the LLM to generate a response that is factually accurate and contextually specific. This method bypasses the need for expensive model fine-tuning and minimizes the risk of AI hallucination, ensuring reliable, high-quality output every time.

https://www.ibm.com/topics/retrieval-augmented-generation