Overview
The difference between a useless AI answer and a genuinely useful one is usually the prompt. Most business users type a request the way they would type a search query, get a generic response, and conclude the tool is overrated. The skill they are missing is the same one behind a good delegation or a good creative brief: stating the goal, supplying the context, and describing what a good result looks like. That skill is entirely learnable, and no technical background is required.
This is a hands-on, foundation course. It deliberately teaches one skill deeply, writing prompts that get useful and reliable results, rather than surveying AI broadly. The gradient starts with why prompts matter and a plain-language model of how AI assistants respond, builds the anatomy of a good prompt, adds iteration as the working method, then applies the skill to core business tasks and hardens it into reliable, reusable templates. Every module includes a hands-on lab and builds on the one before, using tasks from your own work.
Who Should Attend
- Business users in any role who use ChatGPT, Claude, Copilot, or a similar assistant
- Teams that want a shared, consistent standard for AI-assisted work
- Anyone who tried an AI assistant, got mediocre answers, and wants to fix that
Developers who want prompting depth for building software should take Prompt Engineering for Developers instead.
Prerequisites
- None. No technical background is assumed
- Access to any mainstream AI assistant and a few real work tasks to practice on
What You Will Learn
- Explain, in plain language, why prompt quality changes answer quality
- Write prompts with a clear goal, context, role, format, and constraints
- Refine answers through iteration instead of settling for the first response
- Apply prompt patterns to core business tasks: writing, summarizing, and analysis
- Judge output quality and harden prompts so they work reliably every time
- Build a personal prompt library and share effective prompts with a team
Course Outline
Day one: the fundamentals of a good prompt
- Why Prompts Matter
- What the assistant does with your words, in plain language
- Why vague requests get generic answers: the briefing analogy
- Lab: run the same task with a weak, a decent, and a strong prompt, and compare the results
- The Anatomy of a Good Prompt
- The working parts: goal, context, role, format, and constraints
- Showing what good looks like: examples and tone
- Lab: build prompts from a checklist for three real tasks from your own work
- Iterating to a Great Answer
- The conversation is the method: follow-ups, corrections, and narrowing
- Asking the assistant to critique and improve its own answer
- Lab: take a mediocre first answer and iterate it into one you would actually use
Day two: applying the skill and making it reliable
- Prompts for Core Business Tasks
- Patterns for writing and rewriting, summarizing, and first-pass analysis
- Adapting a pattern to your role and your audience
- Lab: build task-specific prompts for the recurring work in your own job
- Reliability: Getting Good Results Every Time
- Why the same prompt can give different answers, and how to tighten it
- Verifying output: catching errors before they reach your name
- Lab: harden one of your prompts until it produces consistent, trustworthy results
- Reusable Prompts and Team Sharing
- Turning a good prompt into a template others can use
- Building and maintaining a shared prompt playbook
- Lab: build a personal prompt library of five templates and trade the best ones with the group
Extended Version
The three-day version keeps the same gradient and adds depth and practice:
- Advanced patterns: multi-step prompts, working with documents, and structured outputs
- Role-based workshops applying the skill deeply to each attendee's function
- Prompting inside workplace tools, connecting to Getting Value from Microsoft 365 Copilot
- A capstone in which each attendee builds, tests, and presents a complete prompt playbook for their role