  Large Language Models (LLMs) have the unique capability to understand and
generate human-like text from input queries. When fine-tuned, these models show
enhanced performance on domain-specific queries. OpenAI highlights the process
of fine-tuning, stating: "To fine-tune a model, you are required to provide at
least 10 examples. We typically see clear improvements from fine-tuning on 50
to 100 training examples, but the right number varies greatly based on the
exact use case." This study extends this concept to the integration of LLMs
within Retrieval-Augmented Generation (RAG) pipelines, which aim to improve
accuracy and relevance by leveraging external corpus data for information
retrieval. However, RAG's promise of delivering optimal responses often falls
short in complex query scenarios. This study aims to specifically examine the
effects of fine-tuning LLMs on their ability to extract and integrate
contextual data to enhance the performance of RAG systems across multiple
domains. We evaluate the impact of fine-tuning on the LLMs' capacity for data
extraction and contextual understanding by comparing the accuracy and
completeness of fine-tuned models against baseline performances across datasets
from multiple domains. Our findings indicate that fine-tuning resulted in a
decline in performance compared to the baseline models, contrary to the
improvements observed in standalone LLM applications as suggested by OpenAI.
This study highlights the need for vigorous investigation and validation of
fine-tuned models for domain-specific tasks.
