Why is GPT-4 Costing You Time? If you spend more time debugging the code generated by an LLM than writing it yourself, you are making one of these 3 critical errors. Your goal is to stop being an AI editor and start being an AI director. 🔴 MISTAKE 1: Blindly Trusting Docstring Validation Simply asking the AI to generate docstrings without a strict format. The AI invents parameters that don’t match the function, leading to immediate technical debt. Define a strict docstring structure (e.g., Google Style) and demand a validation test.✍️ The Correction Prompt: “Generate the Python code for this task. The docstring must use Google Style and be verifiable by the Pydoc validator. If the docstring contains a type error with the code, generate an alert and correct it.” 🔴 MISTAKE 2: The Flawed Unit Test Prompt Asking for “unit tests” without specifying edge cases. The AI generates basic tests that fail in real-world scenarios (network errors, timeouts).The Fix (TDD-AI Approach): Demand tests that cover failure scenarios.✍️ The Correction Prompt: “Using Pytest, create unit tests for the code above. 1. Must include an edge case test (ValueError if input is negative). 2. Simulate a network mock for a 5-second timeout test. 3. Use assert for every scenario.” 🔴 MISTAKE 3: The Copy-Paste Fallacy (No Context) Copying and pasting code without reviewing dependencies. The AI can use outdated libraries that your Python environment doesn’t support. Always define your environment and restrictions.✍️ The Correction Prompt: “Generate the code. Only use Python 3.10 or higher, and do not include any dependencies that are not in the standard library (or specify the exact versions of external libraries in a requirements block).”