# Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external authoritative knowledge bases before generating responses. LLMs, trained on extensive data with billions of parameters, handle tasks like Q\&A and translation. RAG tailors LLMs to specific domains or internal knowledge without retraining, making it a cost-effective method for maintaining relevant, accurate, and useful output. RAG offers benefits like cost-effective implementation, current information updates, enhanced user trust, and more developer control. It works by augmenting user input with retrieved data, improving the accuracy and relevance of responses. For more details, you can visit the article [here](https://aws.amazon.com/what-is/retrieval-augmented-generation/). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.gonesis.ai/neuro-net/ai-creator-platform/g-kb-gonesis-knowledge-base/retrieval-augmented-generation-rag.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.