How Microsoft Copilot Replaced 40 Hours of Work in Just 5 Minutes

How Microsoft Copilot Replaced 40 Hours of Work in Just 5 Minutes 

A client of mine had a real challenge. They were managing 95 corporate policies written over different years by various departments. Some policies even contradicted each other. It was a compliance risk, but no one had time to read through thousands of pages. 

They asked, can #Microsoft Copilot help? It turns out it could. We took these steps.

1. Open Microsoft Copilot Pro's Research Agent 
2. Connected it to their SharePoint folder where all the policies were stored.
3. I gave it a single prompt: 
• Search all policies and identify where any two contradict each other. Return a table with this content: 
• Column A: Policy A include the sharepoint file link + summary description 
• Column B: Policy B include the sharepoint file link + summary description 
• Column C: The issue or contradiction found 
• Column D: Provide a recommended action based on industry and policy best practices.”

In about five minutes, Copilot returned a full table. It highlighted conflicts, provided file links, and suggested next steps based on best practices. It was not perfect, but it was a powerful starting point compared to spending 40 hours on manual review.  Now the team scheduled this prompt to run monthly. Copilot scans new or updated policies and highlights issues automatically. Once they trusted the output, they shared the Agent with others across the company to keep policy management clear and fast. 

 Lesson: You do not need a huge budget to save serious time. Teach your team how to think with AI and real value will show up in everyday work. So what has your team done with AI this week?

Your Phone Is About to Get Its Own AI

Title: Your Phone will Get Its Own AI

A new article from Harvard Business Review, The Case for Using Small Language Models, highlights an important shift. Instead of relying on large cloud-based AI systems, we are moving toward smaller, faster models that can eventually run directly on your phone.

These are called small language models, or SLMs. They are simplified versions of the AI behind tools like ChatGPT, but built to run locally. That means they respond quickly, protect your privacy, and do not need a constant internet connection.

This shift is possible because hardware is catching up. Companies like NVIDIA are building powerful AI systems small enough to fit in edge devices like phones and wearables.

The result is a future where your phone could have its own private AI assistant. One that understands your habits, communicates in your style, and keeps your data on your device.

It is not here yet, but it is coming. And it is going to change how we interact with technology.

Read the full article here: The Case for Using Small Language Models

10 Hard-Learned Lessons in Prompt Engineering

A recent post on Reddit’s r/PromptEngineering shared ten powerful lessons learned from real-world experience. It’s a goldmine for anyone building working with AI and building CustomGPT's or Agents. It is also great fundamental advice.

Key Insights 

1.  Examples beat instructions. Models learn patterns faster from examples than from detailed written rules.
2. Treat prompts like code. Use version control, testing, and performance tracking.
3. Test coverage > prompt polish. Wide evaluation exposes hidden weaknesses better than clever phrasing.
4. Domain expertise wins. Subject experts write more accurate prompts than general engineers.
5. Don’t overlook temperature. Small tuning can solve major consistency problems.
6. Every model is unique. What works for GPT-4o may fail on Claude or Llama.
7. Keep reasoning simple. Complex “chain-of-thought” prompts don’t always outperform direct instructions.
8. Use AI to optimize AI. Models can often refine their own prompts effectively.
9. Strong system prompts matter most. Foundation setup drives most of the output quality.
10. Plan for prompt injection defense early. Secure prompts before deployment. These insights reflect a maturing field: prompt engineering is evolving from creative experimentation into disciplined software engineering. 

Credit: Original insights created and posted by a community member on Reddit’s r/PromptEngineering. 

Labels: Prompt Engineering, AI Development, LLM Optimization, Machine Learning, Best Practices