  Generating high-quality answers consistently by providing contextual
information embedded in the prompt passed to the Large Language Model (LLM) is
dependent on the quality of information retrieval. As the corpus of contextual
information grows, the answer/inference quality of Retrieval Augmented
Generation (RAG) based Question Answering (QA) systems declines. This work
solves this problem by combining classical text classification with the Large
Language Model (LLM) to enable quick information retrieval from the vector
store and ensure the relevancy of retrieved information. For the same, this
work proposes a new approach Context Augmented retrieval (CAR), where
partitioning of vector database by real-time classification of information
flowing into the corpus is done. CAR demonstrates good quality answer
generation along with significant reduction in information retrieval and answer
generation time.
