Testing and Refining Your Tool: The Iterative Debugging Loop

Building a custom GPT or Gem is not a "One-and-Done" task. It requires a rigorous Iterative Debugging Loop to ensure the instructions are followed under stress. In this lesson, we learn how to "Stress-Test" your commands and refine them for 100% production fidelity.

🏗️ The Stress-Testing Framework

Ambiguity Test: Give the model a vague input (e.g., "Analyze this" without a URL). Does it ask for missing data or hallucinate?
Constraint Test: Purposely use a 'Forbidden Word' in your query. Does the model call you out or ignore the rule?
Volume Test: Paste a 5,000-word transcript. Does the model maintain its persona at the end of the summary?

🛠️ Technical Snippet: The 'Error Correction' Prompt

If your tool is failing, add this "Correction Layer" to your system prompt:

### ERROR HANDLING LOGIC
- If the input is missing a URL, respond: "ERROR: TARGET_MISSING. Please provide a domain for audit."
- If the logic requires external data you cannot access, state: "DEPENDENCY_FAILURE: [API Name] required."
- Never apologize. State the error code and the required fix.

🔍 Nuance: Logit Bias

Some models allow you to adjust Logit Bias—the probability of certain words appearing. While you can't always set this in a GUI like GPTs, you can simulate it with negative prompting (as seen in Lesson 2.2) to "Force" the model toward more professional technical vocabulary.

⚡ Practice Lab: The "Broken" Command

Setup: Create a simple prompt that summarizes news.
Break: Paste a recipe instead of news.
Fix: Add a "Type-Check" instruction to your prompt: "Verify the input is a news article. If not, reject the task with code ERR_INVALID_TYPE."
Verify: Rerun the recipe test and ensure the model correctly rejects it.

📝 Homework: The Production QC Report

Take your "Agency Wiki" Gem from Lesson 3.2. Run 5 stress tests on it. Document which tests passed and which failed. Refactor the instructions until all 5 tests return a "PASS" status.