Base models are powerful but they don’t solve every problem out of the box. This session breaks down when fine-tuning is actually worth the effort, and when simpler approaches perform just as well. We'll wwalk through a practical decision framework for choosing between prompt engineering, RAG, fine-tuning, or building a model from scratch grounded in real failure cases teams run into in production. We'll also compare full fine-tuning and parameter-efficient methods (PEFT), unpacking the trade-offs across cost, compute, and data requirements. The session also explores how data quality outweighs sheer volume, how to create high-signal training examples, and where synthetic data can (and can’t) help. Key takeaways → How to decide when fine-tuning is the right tool and when it isn’t. → Practical trade-offs between prompt engineering, RAG, PEFT, and full fine-tuning. → What actually matters in training data quality and evaluation.