Fine-tuning intro: when it's worth it and what to try first

Teams often spend weeks and money fine-tuning a model, then find out a better prompt or the right documents would have done the job.

In this free 75-minute live session, Nikalina Ogorodova, an AI/ML engineer at Hyperskill who has trained over 300 engineers, covers:

1. How LLMs are trained and where fine-tuning fits.
2. What to try first: better prompts, examples, or access to the right documents.
3. How to decide if fine-tuning pays off: the data you need, the cost, and how to check it helped.

The session runs on Zoom through Maven. To get the join link and the recording, register here: https://maven.com/p/c99d48/fine-tuning-intro-for-practitioners-and-the-curious

Want a head start? This short video covers when fine-tuning pays off and when prompting or RAG is enough: https://youtu.be/967KtpjBczI