Competitive Prompt Engineering for Accuracy Exercise

Purpose

AI helps to amplify people's thinking. This exercise shows how smart users guide AI step by step. It shows why thoughtful, precise prompting is now a core workplace skill across industries. This hands-on competitive exercise teaches how prompt quality affects accuracy, particularly in multi-step reasoning or numerical tasks. Delegates learn that effective prompting requires clear logic, structured thinking and precise instructions, not just asking a good question. Ideal for professionals in data, consulting, marketing, leadership and L&D.

Objective

Teams compete to write the most accurate, well-structured AI prompt for solving a complex problem involving data or multi-step reasoning. The winning team is the one whose prompt produces the most accurate and complete answer. This can be on the first try or optionally with multiple prompts.

What You Need

• A preselected complex prompt challenge. Choose problems based on your training needs.
• Access to ChatGPT or other advanced AI tools.
• A shared display to show team prompts and AI responses.
• Optionally, provide devices with ChatGPT to each team so they can do prompting on their own.
• A printed or projected rubric for scoring answers on accuracy, completeness and structure.

Setup

• Divide delegates into pairs or groups of 3.
• Present a complex scenario that requires AI to solve a problem accurately. All teams work from the same challenge.
• Explain that the objective is to engineer a single prompt that leads the AI to solve the problem correctly and clearly on its first attempt, no follow-ups.
• Provide guidance or a quick refresher on advanced prompting strategies. Consider the following or any new methods as this is a face-paced active field:

Chain-of-thought prompting: “Think step by step…”

Role prompting: “You are a data analyst at a retail company…”

Constraints: “List exactly 3 reasons and format the answer in bullet points.”

• Get teams to write their prompts and submit it to you (10 minutes).
• Input each prompt into ChatGPT or other AI tools and screenshare the results in real time.
• Analyse one prompt at a time. Show the prompt and the generated results and get the class to evaluate it based on:
  • Accuracy of final answer
  • Completeness of reasoning
  • Clarity and formatting
  • Appropriateness of tone or assumptions
• Once all prompts have been processed and results analysed, get the class to vote on the best prompt/answer pair. Then lead the discussion to see why that prompt was good.
• Follow with a general discussion on AI prompt engineering.

Timing

Explaining the Exercise: 5 minutes

Activity: 10 min Prompt Engineering + 20 min AI Execution & Review = 30 minutes

Group Feedback: 10 minutes

Discussions

• What prompt features produced the best responses?
• How did role or chain-of-thought prompts affect clarity?
• What assumptions did the AI make, and how could your prompt have prevented that?
• How did specificity or constraints improve the result?
• What are real-life contexts where this kind of precision matters?

Variations

• Rapid Round: Give teams 3 minutes to iterate a second version after seeing results.
• Solo Prompting: Get individuals to compete using their own prompts.
• Prompt Repair: Showcase a ‘bad’ prompt and ask teams to fix it.
• Multi-prompts: Allow teams direct access to ChatGPT or other generative AI and get them to use multiple prompts to eventually get to one ideal answer. Then bring back all and compare their final results. Reflect on process.

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