You Probably Don’t Need Fine-Tuning

Jumping onto the fine-tuning ship often feels like the natural next step when prompts hit a corner case. With the rise of SaaS tools simplifying fine-tuning, it’s tempting—but it’s often unnecessary.

Instead of investing time in dataset generation, cleaning, and splitting, focus on refining prompts. Use larger models or plugins designed to optimize prompt writing. Often, better prompt engineering can outperform the results of fine-tuning. Or, consider hybrid approaches. Combining LLMs with classical ML techniques like K-means clustering or decision trees can extend and enhance model outputs effectively.

The real culprit behind the urge to fine-tune? A lack of benchmark datasets. Without proper benchmarks, it’s hard to measure progress or ensure improvements. Establish benchmarks early to avoid running in circles.

Lastly, fine-tuning can be time-intensive. By the time you’ve tailored a model, newer, faster, and cheaper models might render the effort redundant. Explore alternatives before committing to fine-tuning—you’ll likely save time and resources.