RAG
Simon BudziakCTO
RAG (Retrieval-Augmented Generation) is a technique used to improve the accuracy, reliability, and relevance of LLMs by retrieving specific data from an external knowledge base before generating a response.
Instead of relying solely on pre-training data (which can be outdated or generic), RAG allows the model to "look up" facts in real-time from your documents, databases, or wikis. This effectively combines the reasoning power of LLMs with specific, proprietary data, significantly reducing hallucinations for business applications.
Instead of relying solely on pre-training data (which can be outdated or generic), RAG allows the model to "look up" facts in real-time from your documents, databases, or wikis. This effectively combines the reasoning power of LLMs with specific, proprietary data, significantly reducing hallucinations for business applications.