  The effectiveness of Large Language Models (LLMs) in generating accurate
responses relies heavily on the quality of input provided, particularly when
employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by
sourcing the most relevant text chunk(s) to base queries upon. Despite the
significant advancements in LLMs' response quality in recent years, users may
still encounter inaccuracies or irrelevant answers; these issues often stem
from suboptimal text chunk retrieval by RAG rather than the inherent
capabilities of LLMs. To augment the efficacy of LLMs, it is crucial to refine
the RAG process. This paper explores the existing constraints of RAG pipelines
and introduces methodologies for enhancing text retrieval. It delves into
strategies such as sophisticated chunking techniques, query expansion, the
incorporation of metadata annotations, the application of re-ranking
algorithms, and the fine-tuning of embedding algorithms. Implementing these
approaches can substantially improve the retrieval quality, thereby elevating
the overall performance and reliability of LLMs in processing and responding to
queries.
