Metis Insight #4

What is Retrieval Augmented Generation (RAG)?

Generative AI services are excelling in generating text responses due to the development of large language models (LLMs), which are trained on vast amounts of data. We interact with these AI services by sending prompts, and they respond based on their pre-trained data. However, a problem arises when the request involves information that wasn't included in the training data.

Over time, the data used to train LLMs becomes outdated and doesn't include newly generated specific information, such as details about an organization's products or services. Just as humans can't provide accurate answers about unfamiliar topics, AI can also produce inaccurate or fabricated information, a phenomenon known as "hallucination."
When faced with a question we don’t know well, we search for information to provide an accurate answer. AI can do the same through a process called Retrieval Augmented Generation (RAG).

Here's a brief overview of how RAG works
(the figure might help you understand it better):

1) User Submits a Prompt: When you submit a prompt, the same query is sent to an embedding model.
2) Embedding Model Converts Query: The embedding model converts the query into an embedding vector.
3) Vector Search in a Large-scale Database: Using this query embedding, the most similar data is searched for in a large-scale vector database. The size and comprehensiveness of the database are crucial, as they ensure the retrieval of accurate and contextually relevant information.
4) Data Retrieval: The retrieved data, along with the original query, is sent to the LLM.
5) Response Generation: The LLM generates a proper response using the query and the retrieved context, which is then returned to the user.

A large-scale database is vital for retrieving the proper context through vector searching. It ensures that the LLM has access to the most accurate and relevant information, thereby improving the quality and reliability of the AI-generated responses. As databases grow and improve, the effectiveness of RAG will continue to enhance, providing more precise and useful outputs.

Additionally, to make the RAG process more efficient, it is essential to expedite the search for the most similar data. This is why vector databases require acceleration. MetisX’s computational memory can enhance the performance of vector databases through memory expansion and near-memory parallel processing.

In our next article, we will explore our acceleration process in greater detail.