Retrieval-Augmented Generation (RAG) is an AI technique designed to improve the accuracy and relevance of responses generated by large language models (LLMs). It does this by enabling the model to retrieve and incorporate information from external, authoritative knowledge bases or documents beyond its original training data before generating an answer. This approach helps overcome limitations of LLMs, such as outdated knowledge, hallucination of facts, or generic responses, by grounding the output in up-to-date and domain- specific information
. RAG works in two main phases:
- Retrieval phase: The system searches for and retrieves relevant information snippets from external sources like databases, internal company data, or indexed documents.
- Generation phase: The LLM uses the retrieved information combined with its internal knowledge to generate a more accurate, contextually relevant, and verifiable response
This method allows organizations to maintain control over the AI's output, reduce the need for costly retraining, and provide transparency by including source references in responses. RAG is particularly useful for applications requiring precise, current, or specialized knowledge, such as chatbots answering domain-specific queries or generating fact-based content
. In summary, RAG enhances generative AI by combining traditional information retrieval with powerful language generation, making AI outputs more factual, relevant, and trustworthy.
