Playbook
Practical guidance for applying AI at work. One piece of a broader enablement system that covers any workplace topic.
Prompt Engineering
When writing prompts, include specific details about what you want. Instead of “write a report,” try “write a 500-word quarterly sales report for Q4 focusing on revenue growth and customer acquisition.”
Example
Bad: “Summarize this” Good: “Summarize this article in 3 bullet points highlighting the main arguments and key statistics”
Productivity
Identify tasks you do repeatedly and create templates or prompts for them. This can save hours each week on emails, reports, and data analysis.
Example
Create email templates for common scenarios, use AI to draft first versions, then personalize.
Data Analysis
For complex analysis, break your question into smaller parts. Ask the AI to analyze each component, then synthesize the results.
Example
Instead of one large query, use: “First, identify trends. Second, find anomalies. Third, suggest actions.”
Content Creation
Don’t settle for the first AI output. Ask for revisions, different tones, or alternative approaches to get the best result.
Example
After getting initial content, try: “Make this more concise” or “Rewrite for a technical audience”
Code Assistance
When asking for code, specify your tech stack, coding style, and any constraints. This leads to more usable solutions.
Example
Include: language version, framework, performance requirements, and coding standards.
Research
Leverage AI to quickly gather information, identify key sources, and create research outlines before diving deep.
Example
Ask: “What are the top 5 recent studies on [topic]? Summarize their methodologies and key findings.”
Browse the course library, or create custom training for your workplace.