  This study presents an innovative enhancement to retrieval-augmented
generation (RAG) systems by seamlessly integrating fine-tuned large language
models (LLMs) with vector databases. This integration capitalizes on the
combined strengths of structured data retrieval and the nuanced comprehension
provided by advanced LLMs. Central to our approach are the LoRA and QLoRA
methodologies, which stand at the forefront of model refinement through
parameter-efficient fine-tuning and memory optimization. A novel feature of our
research is the incorporation of user feedback directly into the training
process, ensuring the model's continuous adaptation to user expectations and
thus, improving its performance and applicability. Additionally, we introduce a
Quantized Influence Measure (QIM) as an innovative "AI Judge" mechanism to
enhance the precision of result selection, further refining the system's
accuracy. Accompanied by an executive diagram and a detailed algorithm for
fine-tuning QLoRA, our work provides a comprehensive framework for implementing
these advancements within chatbot technologies. This research contributes
significant insights into LLM optimization for specific uses and heralds new
directions for further development in retrieval-augmented models. Through
extensive experimentation and analysis, our findings lay a robust foundation
for future advancements in chatbot technology and retrieval systems, marking a
significant step forward in the creation of more sophisticated, precise, and
user-centric conversational AI systems.
