Citation:

Satyadhar Joshi. "Advancing the Safety, Performance, and Adaptability of Large Language Models: Review of Fine-Tuning and Guardrails" International Research Journal of Economics and Management Studies, Vol. 4, No. 2, pp. 253-261, 2025.

Abstract:

Large Language Models (LLMs) have transformed natural language processing, allowing for applications in a wide range of domains. Optimal tuning and evaluation of LLMs for a given task, however, remains a considerable challenge. The paper presents a detailed overview of fine-tuning methods, guardrails for secure AI deployment, and observability tools for the monitoring of LLM performance. We integrate the latest progress, state-of-the-art practices, and open issues in the area, providing a guide to researchers and practitioners on how to improve LLM applications. In this paper, we provide an extensive review of the latest developments in Large Language Model (LLM) applications, with emphasis on three main aspects: AI safety guardrails, fine-tuning approaches, and observability systems. We examine current workgroup contributions according to thematic relevance and explore directions for future work. Besides that, we venture into new areas of research that intersect these spaces, providing an integrated view of the future of LLM. The paper pinpoints loopholes in existing methods and proposes innovative approaches to bettering LLM performance, security, and versatility. Large Language Models (LLMs) have shown impressive feats in various applications. Nonetheless, their full utilization demands proper planning for safety, reliability, and performance. This article integrates existing research and best practices around two essential areas of LLM application development: guardrail implementation and fine-tuning. We discuss the rationale for using these methods, outline different strategies, and emphasize the need for monitoring and assessment. This research seeks to offer a complete description of how these methods can be integrated to build strong and efficient LLM-based solutions.

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Keywords:

Large Language Models, LLMs, Guardrails, Fine-tuning, Evaluation, Monitoring, AI Safety, Natural Language Processing.