Iterative Design: Prompting as a Science

Your first prompt will rarely be perfect. In the industry, we use an Iterative Loop to refine prompts until they consistently produce the right result.

1. The 3-Step Iteration Loop

Draft: Write your initial instruction based on your goal.
Evaluate: Run the prompt. Did the AI hallucinate? Was the tone too long? Did it miss a requirement?
Refine: Edit the prompt to address the failure point and try again.

2. Dealing with Failure Points

Too Chatty?: Add "Be concise. Use bullet points."
Wrong Logic?: Add "Show your thinking step-by-step."
Ignored a Rule?: Move the rule to the end of the prompt (Models often "forget" instructions in the middle of long prompts).

3. Visualizing the Loop

graph TD
    Start[Draft Prompt] --> R[Run Prompt]
    R --> Eval{Does it meet criteria?}
    Eval -->|No| Fix[Refine Instructions]
    Fix --> R
    Eval -->|Yes| Done[Final Prompt Template]

4. Prompt Versioning

Save your good prompts! When you find a set of instructions that works perfectly, copy it into a "Prompt Library" (a simple notes file or a tool like Google Keep).

💡 Guidance for Learners

Prompting is an experiment. Don't get frustrated if the AI fails the first time. Treat every "Fail" as data on how to make the instruction more specific.

Summary

Prompting is a cycle of refinement, not a one-shot task.
Evaluation is look for hallucination, tone, and formatting errors.
Refinement involves adding specific constraints or logic steps.
Prompt Libraries save you from reinventing the wheel every day.

Module 3 Lesson 5: Iterative Prompt Design