What is RAG?
RAG refers to Retrieval Augmented Generation.
Retrieval-augmented generation (RAG) is an advanced framework that enhances the capabilities of generative AI, particularly Large Language Models (LLMs) like GPT, by integrating external data retrieval into the generative process. This approach allows the AI to produce more accurate, relevant, and up-to-date responses by grounding its output in external knowledge sources.
How RAG API Works
RAG operates in two main phases: retrieval and content generation. During the retrieval phase, the system searches a vector database for information relevant to a given query. This database is designed to handle dynamic and flexible data requirements, surpassing the capabilities of traditional structured databases. Once the relevant context is retrieved, it is combined with the user's query and sent to the LLM, which then generates a response that is timely, accurate, and contextually appropriate.
RAG API vs. Fine-Tuning
Unlike fine-tuning, where the model itself is modified to improve performance, RAG optimizes output without altering the underlying LLM. This is particularly beneficial as it allows the AI to ingest targeted information that can be more specific to a particular organization or industry.
Applications and Benefits
RAG is model-agnostic and domain-agnostic, making it suitable for a wide range of applications. It can incorporate data from various sources, including internet data streams, media newsfeeds, and transaction logs, to provide a comprehensive and up-to-date knowledge base for the LLM. This versatility also allows RAG to be fine-tuned for specific use cases such as text summarization and dialogue systems.
Transparency and Trust
One of the key advantages of RAG is that it allows generative AI models to provide sources for their responses, similar to citations in research papers. This promotes transparency and trust, as users can verify the accuracy of the information provided by the AI.
Implementation and Accessibility
RAG does not require extensive infrastructure like a data center and can be implemented on various platforms.