Course Description

This workshop will equip participants with actionable prompt engineering skills, enabling them to effectively use Microsoft Copilot within its current limitations to streamline tasks, enhance outputs, and leverage AI assistance across various applications.

Target Student

Professionals interested in optimizing their use of Microsoft Copilot in daily workflows.

Pre-requisites

Basic familiarity with Microsoft Copilot.

Course Objectives

• Apply Prompt Engineering in Workflows: Learn to design clear, effective prompts for workflows, maximising Copilot's potential to streamline tasks and boost productivity.
• Use Structured Frameworks for Complex Tasks: Apply frameworks like RTF and RISEN to handle multifaceted tasks with clarity and precision in prompt engineering.
• Work Within Copilot's Limitations: Become skilled at using Copilot within current limitations, using strategies for prompt refinement to ensure quality outputs.
• Build a Collaborative AI Mindset: Understand the role of human intervention with Copilot, fostering an iterative approach for continuous improvement in workflows.

Course Content

• The Basics of Prompt Engineering
  A top-level introduction to prompt engineering, focusing on the importance of crafting clear, structured prompts to guide AI tools effectively.
• Prompt Engineering 101: Input, Context, and Examples
  Introduce participants to the fundamentals of prompt engineering, focusing on providing input, context, and relevant examples to optimise Copilot's responses.
• Frameworks for Structuring Prompts
  Explore frameworks like RTF (Role, Task, Format) and RISEN (Role, Instructions, Steps, End Goal, Narrowing), demonstrating how they help achieve clearer, more actionable outputs from Copilot.
• Prompt Chaining for Complex Tasks
  Introduce prompt chaining to break down multi-step tasks into sequential prompts, illustrating how this approach works within Copilot's limitations.
• Moving from Concept to Action
  Provide guidance for participants to practise creating effective prompts, applying frameworks, and using prompt chaining to simulate real-world tasks.