Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows

• URL: http://arxiv.org/abs/2505.24189v2
• Date: Wed, 16 Jul 2025 21:38:06 GMT
• Title: Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows
• Authors: Orlando Marquez Ayala, Patrice Bechard, Emily Chen, Maggie Baird, Jingfei Chen,
• Abstract summary: Fine-tuning Small Language Models (SLMs) for real-world applications may no longer be clear.<n>We compare fine-tuning an SLM against prompting LLMs on the task of generating low-code in form.<n>We observe that while a good prompt can yield reasonable results, fine-tuning improves quality by 10% on average.
• Score: 1.6163129903911508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) such as GPT-4o can handle a wide range of complex tasks with the right prompt. As per token costs are reduced, the advantages of fine-tuning Small Language Models (SLMs) for real-world applications -- faster inference, lower costs -- may no longer be clear. In this work, we present evidence that, for domain-specific tasks that require structured outputs, SLMs still have a quality advantage. We compare fine-tuning an SLM against prompting LLMs on the task of generating low-code workflows in JSON form. We observe that while a good prompt can yield reasonable results, fine-tuning improves quality by 10% on average. We also perform systematic error analysis to reveal model limitations.

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