RAG, or Retrieval-Augmented Generation, is an AI framework designed to enhance the quality of responses generated by language models. It achieves this by integrating external knowledge sources, which helps bridge the gap between the capabilities of language models and real-world information. This results in more contextually informed and reliable text generation, making it particularly useful for applications that require accurate and relevant information.

The RAG process can be divided into several key steps, including indexing, prompting, searching, retrieving, and generating responses. While creating a prototype for a RAG application is relatively straightforward, optimizing it for performance, durability, and scalability when dealing with large knowledge databases can be quite challenging ⁴.

Additionally, advanced RAG techniques have emerged to address specific challenges, such as handling multimodal data (e.g., images and charts) and improving the accuracy of responses. Techniques like Small to Big RAG and Small to Slide RAG focus on enhancing the precision of searches while ensuring that the generated responses maintain meaningful context ⁵.