Class Introduction
This was the first session of a Microsoft Copilot training program focused on Excel proficiency, led by Dinesh with participants including Angela, Hany, Alan, and others. The class began with practical exercises using a dataset from TeamAcademy.click/AI-dataset, where participants downloaded and opened Excel sheets including customer feedback and balance sheet data to practice basic Copilot functionality. Dinesh explained the fundamentals of effective prompting, covering the key components of good prompts including task definition, context, persona, format, and tone, using agricultural planning and financial analysis as practical examples. The session included detailed discussions about single-shot versus follow-up prompts, with participants collaboratively developing better prompt structures for predictive analysis and forecasting. Technical issues were addressed throughout, including subscription setup and browser compatibility problems, with Dinesh offering to provide individual support for participants needing Copilot licenses. The training emphasized that successful AI implementation requires clear objective definition, appropriate context setting, and proper customization of templates rather than relying solely on automated prompt engineering tools.
Microsoft Copilot Excel Training
The team began a Microsoft Copilot training program focused on Microsoft Excel, with Dinesh leading the session and explaining they would cover advanced features including creating tables, data analysis, and prediction forecasting. The session started with participants being directed to access a dataset at TeamAcademy.click/AI-datasets, but several attendees including Hany and Angela experienced technical difficulties accessing the link. The session was in its early stages with participants being guided through the login process and dataset selection, though the main training content had not yet commenced.
Microsoft Copilot Excel Training
The team conducted a Microsoft Copilot training session focused on Excel implementation. The instructor guided participants through accessing and downloading a practice dataset containing four Excel sheets (balance sheet, construction project medium data, construction project risk register, and customer feedback) from a SharePoint site. Participants were instructed to download the files to their OneDrive and extract them to practice with Copilot functionality, with the session specifically focusing on opening the customer feedback Excel sheet as an initial exercise.
Microsoft Copilot for Feedback Analysis
The team discussed using Microsoft Copilot in Excel to perform qualitative analysis of customer feedback data. They explained how to access and use the Copilot feature, emphasizing the need for a proper subscription. The instructor demonstrated how to create prompts to analyze feedback text, categorizing comments as positive, neutral, or negative, and showed the resulting analysis of 50 store visitors with 46% positive feedback, 28% neutral, and 26% negative.
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Microsoft Copilot Best Practices
The team discussed using Microsoft Copilot for generative AI tasks, focusing on how to properly structure prompts and handle variations in AI outputs. Team members Abdullah and Hany asked questions about categorization results and consistency, with the instructor explaining that different categorizations are acceptable and can be controlled by providing specific definitions in the prompts. The discussion covered best practices for breaking down complex tasks into smaller objectives and using clear, specific commands when interacting with AI tools. The instructor emphasized the importance of dividing tasks into multiple prompts and demonstrated how to request specific charts and graphs from AI, though they decided to focus on Excel functionality rather than PowerPoint presentation creation in this session.
Microsoft Copilot for Financial Analysis
The team discussed using Microsoft Copilot for Excel analysis, with a focus on interpreting balance sheets and converting financial data into KPIs. The instructor explained that the goal is to use AI for predictive analysis rather than just descriptive analysis, covering three main objectives: interpreting balance sheet data, converting it into measurable KPIs, and forecasting future performance. The team was asked to practice using Copilot with a specific balance sheet exercise, with the instructor providing detailed prompts and emphasizing the importance of breaking down complex analysis into manageable objectives.
Prompt Engineering Best Practices Training
The team discussed prompt engineering best practices, with Team explaining the key components of effective prompts including task, context, example, persona, format, and tone. Hany requested more training on prompt engineering beyond just using Copilot, specifically asking for examples and templates to help team members create better prompts for their specific domains. Team agreed to provide additional resources including a Persona Library with 300+ virtual personas and prompt playbooks available at TeamAcademy.com/AI dataset, and committed to providing more examples as the training progresses.
Copilot Technical Setup Discussion
The team took a 10-minute break to accommodate prayer time, with instructions to return by 3:35pm. Angela raised concerns about a technical issue with Copilot.com that showed a suspicious download prompt, which the team agreed to investigate after the session. The instructor planned to work with Alan, Mohammed Muqeet, and Munzah to set up Copilot basics, and then provided an example prompt about agriculture to demonstrate proper prompt engineering techniques.
AI Prompt Engineering for Agriculture
The team discussed creating effective prompts for AI, focusing on agriculture as an example. They explored the components of good prompts, including task definition, context, persona, format, and tone. The instructor emphasized the importance of breaking down complex tasks into multiple objectives rather than using single-shot prompts. They also covered the concept of follow-up prompting and the need to maintain a clear thread of conversation for better AI responses. The discussion included identifying examples, contexts, and personas in sample prompts, with the instructor clarifying that slide number 6 contained intentionally incorrect mappings to test the team's understanding.
AI Prompt Engineering for Finance
The team discussed prompt engineering for AI analysis, focusing on creating effective prompts for financial data analysis. They examined how to structure prompts with clear tasks, context, and objectives, while emphasizing the importance of benchmarking against standards and frameworks. The instructor explained that while AI can provide templates, prompts should be customized by the user to achieve specific outcomes. As next steps, participants were assigned to create three specific business domain prompts before the next class, and the instructor offered to help with Copilot license procurement for those needing assistance.
Learn how to use Microsoft Copilot in Excel for customer feedback analysis, financial reporting, KPI generation, and business forecasting. Build practical prompt engineering skills and apply AI to real business data with Team Academy.
