RAG vs. Fine-Tuning vs. Prompt Engineering: A Comparative Analysis for Optimizing AI Models
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805004Keywords:
Large Language Models, Retrieval Augmented Generation, Fine-Tuning, Prompt Engineering, AI Optimization, Natural Language Processing, Vector Databases, Machine Learning, Healthcare AI, Finance AI, Dialog SystemsAbstract
The advent of Large Language Models (LLMs) has revolutionized Artificial Intelligence capabilities, enabling complex natural language understanding and generation tasks. However, deploying these powerful, general-purpose models effectively for specific domain-centric applications requires tailored optimization strategies. This paper presents a comparative analysis of three prominent techniques: Prompt Engineering, Fine-Tuning, and Retrieval-Augmented Generation (RAG). We delve into the mechanisms, practical implementations, strengths, and limitations of each approach. Utilizing real-world examples from industries like healthcare and finance, we illustrate how these methodologies address domain adaptation, knowledge integration, and performance enhancement. The analysis provides insights for practitioners navigating the landscape of LLM optimization and serves as a valuable guide for organizations seeking to transition from rigid, rule-based systems like dialog trees to more flexible, knowledge-aware LLM frameworks. Keywords are provided to facilitate indexing and search within the technical literature.
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