Retrieval Augmented Generation Rag
Retrieval Augmented Generation (RAG) is an architectural framework that enhances generative AI models by integrating external information retrieval mechanisms. Rather than relying exclusively on the static knowledge encoded within a model’s parameters during training, RAG systems query external data sources to gather relevant context before generating a response. This approach addresses the limitations of large language models regarding knowledge cutoffs and factual accuracy by grounding outputs in up-to-date, specific information.
The process typically involves two main stages: retrieval and generation. During the retrieval phase, the system converts the user’s query into a vector embedding and searches a vector database for semantically similar documents or data chunks. These retrieved pieces of context are then combined with the original query and passed to the large language model. The model uses this augmented context to generate a response that is both coherent and factually grounded in the provided source material.
This architecture allows organizations to leverage proprietary or private data without the need for expensive and computationally intensive model retraining. By decoupling knowledge storage from the model itself, RAG enables dynamic updates to the underlying information base. Consequently, the system can provide accurate answers based on the latest available data while maintaining the linguistic capabilities of the underlying generative model.
Source Notes
- 2026-04-07: Karpathy’s LLM Wiki: Watch Me Build a Knowledge Base From
- 2026-04-08: Next Evolution of Retrieval-Augmented Generation
- 2026-04-10: Karpathys LLM Wiki Beyond RAG for Persistent Knowledge Bases · ▶ source