Prompt Engineering

Prompt Engineering

Duration: 1 Day

Description

Prompt engineering is the practical skill of getting reliable, high-quality results from generative AI tools. This hands-on course builds on a basic understanding of generative AI and teaches a repeatable approach to writing, structuring, and refining prompts for real work. Participants learn the patterns behind effective prompts, how to give models the right context and constraints, and how to troubleshoot when results fall short. By the end of the day, attendees can design prompts for their own recurring tasks and adapt them as tools and needs change.
Class time is divided between discussion (40 percent) and hands-on prompting exercises (60 percent).

Audience

Business professionals, knowledge workers, and anyone who already uses or plans to use AI assistants and wants to get consistently better results. A basic familiarity with generative AI tools is helpful but not required.

Objectives

  • Describe how large language models interpret prompts and why phrasing matters
  • Apply a repeatable structure for writing clear, effective prompts
  • Use context, examples, and constraints to steer model output
  • Iterate and refine prompts to improve accuracy and consistency
  • Recognize and correct common prompting failures and limitations
  • Build reusable prompt patterns for recurring business tasks

Prerequisites

There are no strict prerequisites, though the following will help participants get the most from the course:

  • Exposure to a generative AI assistant such as a chat-based tool
  • Comfort using a computer and web-based applications

Course Outline

  • Module 1 – Course Introduction
    • Welcome and objectives
    • What prompt engineering is and is not
    • How models read and respond to prompts
  • Module 2 – Anatomy of an Effective Prompt
    • Role, task, context, and format
    • Being specific: clarity over cleverness
    • Setting tone, length, and audience
    • Common building blocks and templates
  • Module 3 – Giving Context and Examples
    • Providing background and source material
    • Few-shot prompting: showing examples
    • Using constraints and guardrails
    • When and how to break a task into steps
  • Module 4 – Iterating and Refining
    • Reading and diagnosing weak output
    • Refining prompts through follow-ups
    • Comparing approaches and keeping what works
    • Hands-on exercise: iterate a prompt to a usable result
  • Module 5 – Prompting Pitfalls and Limits
    • Hallucinations and how to reduce them
    • Ambiguity, bias, and over-broad requests
    • Verifying and fact-checking output
    • Knowing when AI is the wrong tool
  • Module 6 – Prompts for Real Work
    • Writing, editing, and summarizing
    • Analysis, research, and brainstorming
    • Building a personal library of reusable prompts
    • Hands-on exercise: prompts for your own tasks
  • Module 7 – Course Wrap-Up
    • Key takeaways
    • Resources for continued learning
    • Next steps