Author(s): Abduldattijo

Originally published on Towards AI.

“What if we’re doing this all wrong?” I muttered to myself as I stared at yet another disappointing set of evaluation metrics. Three weeks into fine-tuning our company’s customer service LLM, and we were still seeing mediocre results at best. Our ROUGE scores had plateaued, and real user feedback consistently mentioned the model’s tendency to hallucinate product details and misinterpret complex queries.

I’d followed all the standard fine-tuning protocols: curated high-quality examples, balanced the dataset, experimented with learning rates, and even tried different model architectures. Nothing moved the needle significantly. As our launch deadline loomed closer, I was desperate enough to try something unconventional.

That desperation led to a discovery that not only tripled our model’s performance but completely changed how I approach LLM fine-tuning. The solution wasn’t in fancy techniques or more compute — it was hiding in plain sight, in an area most tutorials and guides completely overlook.

Like most ML engineers, I’d been indoctrinated into the conventional fine-tuning paradigm: collect a dataset of examples that represent your target task, format them as instruction-response pairs, and train the model to minimize the difference between predicted and target outputs.

Our dataset consisted of 15,000 carefully selected customer service interactions — real questions from… Read the full blog for free on Medium.

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Published via Towards AI