What is Retrieval-augmented Generation?

Fine-tuning involves training a pre-trained large language model (LLM) on a specific dataset to adapt it for particular tasks or domains. This process adjusts the model’s parameters based on the new data, but it can be time-consuming and resource-intensive.

Retrieval-augmented generation (RAG), on the other hand, dynamically retrieves relevant, up-to-date information from external sources during the language generation process. This allows RAG models to incorporate new data without needing to alter the model’s underlying parameters, making it more flexible and scalable for knowledge-intensive tasks.

Retrieval-augmented generation (RAG) is a framework that combines large language models with an information retrieval system to enhance their ability to generate accurate and contextually relevant responses. It works by retrieving relevant documents or data from an external knowledge source, such as a vector database, and using this information to generate more precise and up-to-date language outputs. RAG models are particularly useful for tasks that require incorporating domain-specific or real-time information, such as answering complex customer queries or handling knowledge-intensive natural language processing (NLP) tasks.

Large language models (LLMs) are general-purpose models trained on vast amounts of data to perform various language generation tasks, but they are limited by the scope and recency of their training data. Retrieval-augmented generation (RAG) enhances LLMs by integrating a retrieval mechanism that accesses external sources of information in real time. This enables RAG models to provide more accurate, relevant, and contextually enriched responses. While LLMs generate language based solely on pre-trained knowledge, RAG models can dynamically incorporate new and specific information, making them more effective for complex and knowledge-intensive applications.