5 Hard-Won Lessons About Fine-Tuning Large Language Models (LLMs)
Summary : Fine-tuning Large Language Models (LLMs) is often misunderstood as a guaranteed path to better performance. In reality, it is a strategic, data-driven, and operational process. My blog post gives five practical lessons learned from real-world fine-tuning client-facing projects, helping you decide when to fine-tune, how to do it efficiently, and what it truly takes to run fine-tuned models in production. Introduction Fine-tuning is widely seen as the ultimate way to customize a Large Language Model. The common belief is simple: if you want an LLM to excel at a specific task or domain, fine-tuning is the answer. You take a powerful general-purpose model and turn it into a focused specialist. In practice, fine-tuning is far more nuanced. It comes with hidden trade-offs, unexpected risks, and operational responsibilities that are easy to underestimate. Moving from a base model to a production-ready, fine-tuned system is not just about more data. It requires careful ...