Fine-Tuning LLMs - From Generic to Specialist: A Practical Walkthrough Online
Generic LLMs are great generalists, but they often fall short on tone or structure when you need them for a specific task. In this tutorial, we'll look at how to fine-tune an LLM to a specific task through additional training. We will discuss what fine-tuning actually does to a model, when it's better than just prompt engineering or other methods such as retrieval-augmented generation (RAG), and how to approach it. We'll walk through the full lifecycle, from framing the problem and preparing your data for fine-tuning, to training, evaluating, and shipping a specialized model. By the end, you'll have a solid grasp of how fine-tuning works and be ready to begin to apply it to your own tasks. Free and open to faculty, staff, and students, this session is led by a K-State graduate student.
This session will not be recorded.
Brought to you by the K-State AI Consulting Service (KAICS).
- Date:
- Thursday, Sep 10 2026
- Time:
- 11:00am - 12:00pm
- Time Zone:
- Central Time - US & Canada (change)
- Campus:
- Virtual
- Online:
- This is an online event. Event URL will be sent via registration email.
- Audience:
- Faculty/Staff Students
- Categories:
- AI Literacy
