Fine-Tuning Large Language Models for Domain-Specific Business Applications

Abstract

Large Language Models (LLMs) like GPT, LLaMA, and Claude are powerful tools for understanding and generating human language. However, their real value in business lies in domain-specific fine-tuning, allowing them to align with unique industry needs, terminologies, and workflows. This chapter explores how fine-tuning through techniques like instruction tuning, RLHF, LoRA, and PEFT enables the creation of highly accurate, efficient, and context-aware systems in sectors such as finance, healthcare, legal, and retail. It also addresses practical aspects such as data preparation, model selection, and evaluation, along with considerations for privacy, governance, and compliance (e.g., GDPR, HIPAA). Real-world case studies demonstrate the success of fine-tuned LLMs in applications like contract review, fraud detection, and personalized customer service. The chapter concludes with guidance on building robust domain-specific LLM pipelines, tackling challenges like data scarcity, domain drift, and hallucination.

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