RAGs-101: An introduction to llamaindex

LLMs have taken the world by storm, they are powerful, versatile and have applications in a wide variety of fields.

However, there's a big downside called hallucination. This basically means that LLMs can produce wrong outputs, make up facts, and their knowledge is limited to the data they were trained on.

This is where Retrieval Augmented Generation (RAGs) becomes Important.

In simple words RAG is a technique to ground your LLMs to generate responses to your queries based on a custom knowledge-base that you provide.

Now multiple question might arise:

• What is the meaning of custom knowledge base?

• What kind of data it can have?

• How is this data stored and fed to an LLM?

• And what it takes to build such a system?