The term “RAG” stands for Retrieval-Augmented Generation.RAG systems can optimize the data without touching or fine-tuning the model.They also provide access to current data.

Just imagine, you have LLM models trained with knowledge from 2010–2022.
When you ask a question about current news, you can only get answers from that 2010–2022 data.
If you want to get answers with more recent information, you have to fine-tune the model or use a RAG system with current data.

Advantages of RAG systems:

✓ You can change the data easily if it is not correct.
✓ RAG does not need to fine-tune the LLM model.
✓ You can upgrade the database of the RAG system without a big cost.

Now, We can design the RAG systems, What we need?

Step-1
Prerequests
.Vector database,(Elasticsearch)
.Pipeline,
.LLM model