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As large language models (LLMs) evolve into autonomous agents, the demands placed on their capabilities are shifting from general-purpose reasoning to domain-specific execution. Enterprises seeking to operationalize Agentic AI must decide how best to enhance and specialize these models to align with their data, workflows, and compliance requirements. Two primary strategies—Retrieval-Augmented Generation (RAG) and fine-tuning—have emerged as leading approaches to extend LLM functionality and reliability in real-world deployments.

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Enhancing LLMs for Agentic AI: RAG vs. Fine-Tuning

  • 章节

  • 发表日期：

    2025年

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  • Sumit Ranjan,
  • Divya Chembachere,
  • Lanwin Lobo

摘要

As large language models (LLMs) evolve into autonomous agents, the demands placed on their capabilities are shifting from general-purpose reasoning to domain-specific execution. Enterprises seeking to operationalize Agentic AI must decide how best to enhance and specialize these models to align with their data, workflows, and compliance requirements. Two primary strategies—Retrieval-Augmented Generation (RAG) and fine-tuning—have emerged as leading approaches to extend LLM functionality and reliability in real-world deployments.