A discussion about RAG, Vector Database , and Their Monetization Potential, as well as an Analysis of Elastic as a Company-Part-1

The title emphasize that these two concepts, RAG and vector databases, cannot be equated simply.

1. What’s RAG?

RAG is a broader concept that refers not just to a specific product or tech stack, but rather a methodology. It strengthens that when answering questions, it can adopt an open-book approach rather than relying solely on memory. This can also be seen as providing AI and large models with means to interact with external information, enabling them to access external data at any time to enhance generated results.

Many technologies can fall under the RAG concept. For instance, using a search API to allow GitHub to search the web when answering questions is essentially a form of RAG. Similarly, traditional databases can implement RAG without relying on vector databases; for example, in an order management scenario, reading business order data from a Snowflake data warehouse and generating order information descriptions through SQL queries can also be considered RAG.

RAG does not necessarily require the use of vector databases, but given that current AI capabilities are primarily focused on large language models, breakthroughs are often centered around language processing. Therefore, one of the best ways to retrieve information related to language is through vector databases. Vector search and traditional keyword search are two primary methods for information retrieval, and as a result, there is a close relationship between vector databases and RAG.

2. RAG’s Potential