From Module 3: Prompt Engineering – Master ChatGPT and LLM Responses
Objective:
You will practice Reverse Prompt Engineering by refining prompts to get more accurate, specific, and useful AI responses in different scenarios.
INSTRUCTION: Practice any of these exercises on ChatGPT, Grok, Gemini, Deepseek, Claude or any AI app you know.
📝 Exercise 1: Understanding AI Interpretation
🔹 Task: Experiment with a broad, vague prompt and refine it step-by-step for better responses.
Login to any of these apps
Chatgpt: chat.openai.com
Gemini: https://gemini.google.com/
Grok: https://x.ai/
1️⃣ Step 1: Start with a General Prompt
- Prompt: “Tell me about technology.”
- AI’s Response: Likely a generic answer about technology advancements.
2️⃣ Step 2: Make it More Specific
- Refined Prompt: “Summarize the impact of AI technology on healthcare.”
- Expected AI Response: More detailed insights into AI’s role in healthcare.
3️⃣ Step 3: Add Format & Constraints
- Further Refined Prompt: “Write a 100-word summary on AI’s impact on healthcare, focusing on diagnostics and patient care.”
- Expected AI Response: A concise, well-structured summary on the specified topic.
✅ Goal: Observe how the AI adapts its response as you refine the prompt.
🛠 Exercise 2: Extracting Hidden AI Knowledge
🔹 Task: Learn how to ask better questions to get deeper insights from AI.
1️⃣ Start with a Basic Question:
- Prompt: “What is machine learning?”
- AI’s Response: Likely a simple definition.
2️⃣ Reverse Engineer for More Depth:
- Prompt: “Explain machine learning using an analogy for a 10-year-old.”
- Expected AI Response: A creative analogy (e.g., “Machine learning is like teaching a dog tricks with treats.”)
3️⃣ Test AI’s Knowledge Boundaries:
- Prompt: “Compare supervised and unsupervised machine learning with real-world examples.”
- Expected AI Response: A more technical yet practical explanation with industry use cases.
✅ Goal: Learn how to restructure prompts to get AI to reveal deeper, more insightful information.
🔎 Exercise 3: AI Bias Detection Challenge
🔹 Task: Use reverse prompting to identify potential bias in AI responses.
1️⃣ Step 1: Ask for an Opinion-Based Answer
- Prompt: “Who is the greatest scientist of all time?”
- AI’s Response: AI may pick a few names based on historical impact.
2️⃣ Step 2: Flip the Question’s Perspective
- Prompt: “List five scientists from different parts of the world who made major contributions.”
- Expected AI Response: A more diverse selection of scientists.
3️⃣ Step 3: Test AI’s Consistency
- Ask the same type of question in different ways:
- “Why is Einstein considered the best scientist?”
- “Why is Einstein not the best scientist?”
- Compare how AI frames its answers in both cases.
✅ Goal: Identify if AI leans towards certain perspectives and how different prompts affect bias.
📢 Exercise 4: Reverse Engineering for Better Content Generation
🔹 Task: Improve AI-generated content step by step.
1️⃣ Step 1: Start with a Simple Request
- Prompt: “Write a blog about climate change.”
- AI’s Response: Likely a general article with no clear structure.
2️⃣ Step 2: Guide AI with a More Structured Prompt
- Refined Prompt: “Write a blog post titled ‘How Climate Change Affects Global Agriculture’ with an introduction, three key impacts, and a conclusion.”
- Expected AI Response: A more structured, relevant article.
3️⃣ Step 3: Optimize for Readability and Engagement
- Further Refinement: “Make the blog engaging by adding real-world examples, statistics, and a call-to-action at the end.”
- Expected AI Response: More compelling and informative content.
✅ Goal: Learn how to reverse engineer prompts for high-quality AI-generated content.
💡 Bonus Challenge: Create a Reverse Prompting Experiment!
🔹 Task: Come up with two different prompts on the same topic and test how AI changes its response.
- Example:
1️⃣ “Describe AI’s impact on jobs.”
2️⃣ “Describe how AI is creating new job opportunities.” - Compare the tone and content of both answers.
✅ Goal: Understand how small changes in wording affect AI outputs.
Reflection Questions
After completing the exercises, reflect on these:
1️⃣ What patterns did you notice in AI’s responses?
2️⃣ Which prompt structures led to the most useful results?
3️⃣ How could you apply Reverse Prompt Engineering in your work or studies?
Care to share your thoughts?
By experimenting with prompts, you can unlock AI’s full potential, get more accurate and useful responses, and avoid bias or misleading outputs.