10 “Foot Guns" for Fine-Tuning and Few-Shots
Let me share a story that might sound familiar.
A few months back, I was helping a Series A startup with their LLM deployment. Their CTO pulled me aside and said, "Jason, we're burning through our OpenAI credits like crazy, and our responses are still inconsistent. We thought fine-tuning would solve everything, but now we're knee-deep in training data issues."
Fast forward to today, and I’ve been diving deep into these challenges as an advisor to Zenbase, a production level version of DSPY. We’re on a mission to help companies get the most out of their AI investments. Think of them as your AI optimization guides, they've been through the trenches, made the mistakes, and now we’re here to help you avoid them.
In this post, I’ll walk you through some of the biggest pitfalls. I’ll share real stories, practical solutions, and lessons learned from working with dozens of companies.