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The GenAI Maturity Curve or You Probably Don't Need Fine Tuning: Kyle Corbitt
Takeaway
Fine-tune only after prompting, RAG and evals have plateaued — premature fine-tuning bakes in bad specs and loses you flexibility.
Summary
- OpenPipe's Kyle Corbitt — who runs a fine-tuning platform — argues most teams shouldn't fine-tune yet.
- Frames a GenAI maturity curve: start with prompting → RAG → tool use → evals, then consider fine-tuning when you have stable specs and traffic.
- Quality-vs-cost graph shows fine-tunes can hit big-model quality at much higher tokens/dollar, but only after upstream pieces are solid.
- Walks through how to know you've reached the fine-tune moment: stable prompts, consistent failures, and enough labeled trace data.
fine-tuningmaturityopenpipe
Original description
Simple instruction prompting? Few-shot examples? Fine-tuning? How do you decide when to do each? In this talk we’ll discuss the emerging concept of the GenAI maturity curve and define the steps along it. We’ll also discuss the concrete triggers you should watch for to indicate that you’re ready to move up to the next step. At OpenPipe we’ve helped customers fine-tune thousands of models (and redirected thousands more who hadn’t hit the trigger points). Let’s talk about how to speed-run that journey! Recorded live in San Francisco at the AI Engineer World's Fair. See the full schedule of talks at https://www.ai.engineer/worldsfair/2024/schedule & join us at the AI Engineer World's Fair in 2025! Get your tickets today at https://ai.engineer/2025 About Kyle Kyle Corbitt is the co-founder and CEO of OpenPipe, the easiest way to train fine-tuned models and deploy them to production. OpenPipe has fine-tuned thousands of customer models, and serves millions of inference requests every day. Before founding OpenPipe, Kyle led the Startup School team at Y Combinator, which was responsible for the product and content that YC produces for early-stage companies. Prior to that he worked in engineering at Google and studied ML at school.