Mastering Microsoft Copilot through powerful prompts

Summary

Charles, a Microsoft 365 trainer, delivers an insightful session on leveraging generative AI prompts for efficiency in tasks. He contrasts standard AI with co-pilots, emphasizing behavioral shifts for effective use. Through live demos, Charles illustrates the application of AI in Word and PowerPoint, highlighting the nuances between different apps' integrations and the importance of positive, simplified prompts for optimal results. The session concludes with a Q&A, addressing practical use cases and the potential of AI to transform workflow.

Takeaways

• 😀 Charles, a Microsoft 365 trainer, is presenting on using prompts with generative AI like Microsoft's Co-Pilot and how it differs from standard AIs such as Chat GPT.
• 📝 He explains the concept of using prompts to generate responses from AI and the common models for constructing effective prompts, such as role, task, format, and context-action-result.
• 🔍 Charles demonstrates how to use AI prompts with Chat GPT and Claude, showing examples of how they can be applied in real-life scenarios like creating a PR campaign or improving SEO rankings.
• 🚀 He discusses the introduction of Co-Pilot in Microsoft applications and its potential to transform work efficiency but also notes the need to adjust expectations and behavior when using it.
• 🤖 Co-Pilot's functionality varies across different Microsoft applications, with some apps like Word allowing more complex prompts while others like PowerPoint may require simpler ones.
• 🔄 Inconsistencies in AI responses are highlighted, where the same prompt can yield different results on different days or across different apps.
• 🛠 Charles advises simplifying prompts and breaking down complex requests into sub-prompts for better results with Co-Pilot.
• 💡 The importance of positive and action-oriented language when constructing prompts is emphasized to guide the AI effectively.
• 🔒 Security and data protection are critical when using AI tools, especially in sensitive sectors like healthcare or the military, where using generative AI must adhere to strict guidelines.
• 📈 Charles showcases a live demo of using Co-Pilot in Word to generate a job spec, compare it with a CV, and even create interview questions, highlighting the time-saving potential of AI.
• 📚 The session concludes with a Q&A, discussing the use cases of Co-Pilot in various departments and the importance of aligning AI capabilities with organizational needs and security policies.

Q & A

What is the purpose of the training session presented by Charles?

The purpose of the training session is to explain how to use prompts for generative AIs, the differences between standard AIs like Chat GPT and Co-Pilot, suggest behavioral changes to consider, and to demonstrate Co-Pilot's capabilities with a live demo.

What are some of the common models or frameworks for constructing prompts in generative AIs?

Some common models for constructing prompts include role-task-format, task-action-goal, before-after-bridge, context-action-result, and role-input-steps-expectations.

How does Charles demonstrate the use of prompts with AI like Chat GPT?

Charles demonstrates the use of prompts by preparing examples and asking Chat GPT to explain what prompts are and to give examples of how they can be used in real-life scenarios.

What is the difference between the free version of Co-Pilot and the licensed version integrated into Microsoft applications?

The free version of Co-Pilot is accessible via web browsers like Bing and does not access tenant data. The licensed version, integrated into Microsoft applications, can access data on the tenant and is designed to work more seamlessly with Microsoft's ecosystem.

Why might the responses from Co-Pilot in Microsoft Word be different from those in PowerPoint or Excel?

Each application seems to have a different Co-Pilot behind it, which means the AI's understanding and response to the same prompt can vary depending on the application being used.

What behavioral changes does Charles suggest when using the licensed version of Co-Pilot?

Charles suggests simplifying prompts, understanding the nuances of each application, recognizing inconsistencies in responses, and adjusting expectations regarding the AI's capabilities.

How does Charles handle the potential for different results when using the same prompt multiple times?

He acknowledges that Co-Pilot may provide different results each time the same prompt is used, and he suggests adjusting expectations and being prepared for variability in responses.

What is the importance of being positive in the language used when interacting with Co-Pilot?

Being positive in language helps Co-Pilot to respond more effectively, as it is better at processing action-oriented, positive requests rather than instructions phrased negatively.

Can you provide an example of how Co-Pilot can be used in a practical work scenario?

Charles demonstrates using Co-Pilot to generate a job specification, compare it with a candidate's CV, identify tailored interview questions, and summarize the candidate's strengths and weaknesses, which can save hours of manual work.

Why might some organizations block access to certain AI tools like Chat GPT?

Organizations may block access to AI tools like Chat GPT due to security concerns, especially when dealing with sensitive client data, to prevent potential data breaches and maintain compliance with data protection regulations.

What is the significance of adjusting expectations when using Co-Pilot in a professional setting?

Adjusting expectations is important because while Co-Pilot can be a powerful tool, it may not always produce the exact results requested. Understanding this can help users leverage Co-Pilot's capabilities more effectively without being disappointed by its limitations.