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....