Mind the gap: a decision framework to get started with training

If you're spending all your time harness engineering and you’re still not meeting your quality bar, your instinct should be to start training your own model to get the job done.

Before you spend a dollar on training, your first job is to measure the gap between your AI’s capability and its consistency. You need a clear diagnosis of what's actually broken and a way to measure whether fixing it is worth the cost. Skip that step, and you risk spending time and money only to land on a failure your evals would have caught for free.

Our upcoming webinar is for anyone staring at an underperforming AI outcomes and wondering: how do I measure that gap, and is fine-tuning the right solution?

Speakers

Sinan Ozdemir | Head of AI Developer Education at Fireworks

Previously Author of 10+ books on LLMs and agentic AI, founder of Kylie.ai (early agentic AI, YC-backed, acquired 2019), AI Lecturer/Author for Pearson + O'Reilly, and your friendly neighborhood ML engineer.

You’ll learn how to:

•Diagnose whether your failure is actually fixable with training, or something else entirely
•Rule out faster, cheaper fixes before committing to a training run
•Build an eval that tells you your real ceiling and floor, before you spend a dollar

Part of Specialized Intelligence, a Fireworks webinar series on training and fine-tuning.