RAG is not answer to everything !

So when should you actually use RAG?

RAG (Retrieval Augmented Generation ) is a system where an LLM retrieves external information before generating a response . Instead of replying only on the trained data , the model searches a Vector DB or PDF or a Website and uses that context to answer .

RAG is beneficial when:

• The information Changes Frequently

• Working on private/ confidential data

• The context is too large for Prompting alone

RAG is not beneficial when the problem does not req any external knowledge . Many of us unnecessarily add the vector db and pipelines to application where the model already performs well using its trained knowledge . Tasks like Grammar Corrections , coding assistances and conversational chatbots often do not benefit from this at all. A poorly designed rAG system can even can make responses worse by retrieving irrelevant or noisy context.

peace out ✌️

Lohith :)