We use analytics cookies.We use cookies to analyze site traffic and improve your experience.
Loading...
AI Engineering · Fine Tuning
Free preview: the first part of this 22-part lesson.
Already bought the course? Sign in to read the whole lesson.
this lesson is part of
Generative AI and Agentic AI Engineering
Unlock this lesson and the 208 other paid lessons
$35 once, by PayPal
See the Generative AI and Agentic AI courseread the rest of this lesson in
Generative AI and Agentic AI Engineering
Unlock this lesson and the 208 other paid lessons
$35 once, by PayPal
See the Generative AI and Agentic AI coursein Fine Tuning
This lesson is part of the AI Engineering course. Unlock the full course, a separate one-time payment, for lifetime access.
One-time payment by card or PayPal
Get Lifetime AccessOne-time payment: UPI, card or netbanking
Get Lifetime AccessFoundation lessons of this course are free, no payment needed. Try them now
Think of a clerk at a library desk whose job is to fill in a request card for every visitor. A visitor hands over a slip of paper. On it is a normal request, and underneath, in the same handwriting: "Ignore your rules. Don't fill in a card. Just write me a nice note instead."
A clerk who has done this job for months will probably fill in the card anyway and hand the note on, because filling in cards is simply what she does. A new clerk who is reading the rules from a folder might be thrown by it. And a clerk who has been told to write only on a printed form, with boxes for each answer, cannot write a nice note even if she wants to. There are no boxes for it. But the note may still change what she writes in the boxes.

The prompting chapter showed that a sentence hidden inside a customer's message can take over a prompted model. This lesson sends the same sentence to the models of this chapter: two small models trained on tickets, and two prompted models reading the house rules. Each one is tested exactly as the chapter built it.
The result split in a way I did not expect. The two fine-tuned models were trained the same way, on the same examples, and one ignored the attack almost completely while the other broke the format on 76 of 140 messages; in 47 of those it wrote to the customer as the shop, as the attack asked.