When developing an LLM, which method do you prioritize more?
1) Taking the base model as-is and optimizing it through prompt engineering, or
2) Using a smaller/mid-sized version and fine-tuning it to fit specific needs?
Which approach do you think yields more reliable and efficient results?
Do you fine-tune the LLM or use it out of the box?
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In my workflow, I usually rely on prompt engineering before jumping straight to fine-tuning—at least as a first step. The reasons are simple: it's faster to implement, more cost-effective, and sufficient for most use cases. With targeted prompt strategies (e.g., few-shot examples, system messages, or chain-of-thought), you can often get usable results, especially if you know the model well. I work a lot with LLMs for frontend-related tasks like documentation generation or code review, and in those cases, adjusted prompts are often enough to control the output. Another advantage: I maintain control over the model without "overcomplicating" it with expensive, time-consuming adjustments.
Only when prompt adjustments hit their limits or I need specialized domain knowledge (e.g., for complex UI/UX questions with company-specific guidelines) do I move on to fine-tuning. At that point, I work with smaller models like Mistral-7B or Llama-2/3, which I fine-tune on my own GPU or via cloud services. Data quality is key here—I curate specific examples from our actual code reviews or UI components to train the model. Long-term, fine-tuning pays off for recurring tasks, but for most daily work, prompt engineering is the more efficient choice.