Create fine-tuned, task-specific models that write like you by teaching models using expert knowledge, tone, and structure - with reference information directly attached to the models themselves using Microsoft 365 Copilot Tuning.

Fine-tuning adds new skills to foundational models, simulating experience in the tasks you teach the model to do. This complements Retrieval Augmented Generation, which in real-time uses search to find related information, then add that to your prompts for context.

Fine-tuning helps ensure that responses meet your quality expectations for specific repeatable tasks, without needing to be prompting expert. It's great for drafting complex legal agreements, writing technical documentation, authoring medical papers, and more - using detailed, often lengthy precedent files along with what you teach the model.

Using Copilot Studio, anyone can create and deploy these fine-tuned models to use with agents without data science or coding expertise. There, you can teach models using data labeling, ground them in your organization's content - while keeping the information in-place and maintaining data security and access policies. The information contained in the task-specific models that you create stay private to your team and organization. Task-specific models and related information are only accessible to the people and departments you specify - and information is not merged into shared large language models or used for model training.

Jeremy Chapman, Director on the Microsoft 365 product team, shows how this simple, zero-code approach helps the agents you build write and reason like your experts—delivering high-quality, detailed responses.

► QUICK LINKS:

00:00 - Fine-tune Copilot

01:21 - Tailor Copilot for specialized tasks

05:12 - How it works

05:57 - Create a task-specific model

07:43 - Data labeling

08:59 - Build agents that use your fine-tuned model

11:42 - Wrap up

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