  Large Language Models (LLMs) generate responses to questions; however, their
effectiveness is often hindered by sub-optimal quality of answers and
occasional failures to provide accurate responses to questions. To address
these challenges, a fine-tuning process is employed, involving feedback and
examples to refine models. The objective is to enhance AI models through
continuous feedback loops, utilizing metrics such as cosine similarity, LLM
evaluation and Rouge-L scores to evaluate the models. Leveraging LLMs like
GPT-3.5, GPT4ALL, and LLaMA2, and Claude, this approach is benchmarked on
financial datasets, including the FinanceBench and RAG Instruct Benchmark
Tester Dataset, illustrating the necessity of fine-tuning. The results showcase
the capability of fine-tuned models to surpass the accuracy of zero-shot LLMs,
providing superior question and answering capabilities. Notably, the
combination of fine-tuning the LLM with a process known as Retrieval Augmented
Generation (RAG) proves to generate responses with improved accuracy.
