The process of adjusting prompts to influence the output of a Large Language Model (LLM) is known
as P-Tuning. This technique involves fine-tuning the model on a set of prompts that are designed to
guide the model towards generating specific types of responses. P-Tuning stands for Prompt Tuning,
where “P” represents the prompts that are used as a form of soft guidance to steer the model’s
generation process.
In the context of LLMs, P-Tuning allows developers to customize the model’s behavior without
extensive retraining on large datasets. It is a more efficient method compared to full model
retraining, especially when the goal is to adapt the model to specific tasks or domains.
The Dell GenAI Foundations Achievement document would likely cover the concept of P-Tuning as it
relates to the customization and improvement of AI models, particularly in the field of generative
AI12. This document would emphasize the importance of such techniques in tailoring AI systems to
meet specific user needs and improving interaction quality.
Adversarial Training (Option OA) is a method used to increase the robustness of AI models against
adversarial attacks. Self-supervised Learning (Option OB) refers to a training methodology where the
model learns from data that is not explicitly labeled. Transfer Learning (Option OD) is the process of
applying knowledge from one domain to a different but related domain. While these are all valid
techniques in the field of AI, they do not specifically describe the process of using prompts to shape
an LLM’s output, making Option OC the correct answer.
