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

Orlando Marquez, Patrice Bechard, Emily Chen, Maggie Baird, Jingfei Chen

Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows: 5 upvotes on Hugging Face Daily Papers, #28 of 42 papers on 2025-06-02. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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