Hey there, Ethan here.
And in the next couple of videos, we'll be implementing Chirag or correctly react based on the correct
rank research paper.
And this is an advanced rank technique that is going to help us get more quality answers when performing
retrieval, augmentation, generation.
And the basic concept of Seerat is pretty straightforward.
We first want to start by taking our query, performing in the vector search, semantic search, and
retrieving relevant documents from our vector store.
After we have all those documents, we want to start and self-reflect, to critique those documents
and to determine whether they are indeed relevant to our original query or not.
If all of the documents are relevant to our query, then this is a happy flow and we simply want to
augment our original prompt and send everything to the LLM like we always do in RAC.
However, if you find document that we find out that are not relevant to our query, of course we want
to filter the null and we also want to perform some external search on the internet and get to more
information.
And to augment our prompt with that real time information we get from online.
And after we have that, we can augment our prompt and send everything to the LN.
And this technique is going to give us much more quality response.