Everything begins with a direct, almost curt message. Gabriele – or GC, as he sometimes signs – reports a problem in the editing module of his application: he can't change the category of concepts once they are created. The tone is that of someone who knows the code and expects an equally precise answer. There's no room for beating around the bush.
ChatGPT responds immediately, identifying the bug: the category field (kc_cat) is modified in the user interface, but in the save_concept() function the updated value is not passed correctly when a new category is chosen. The answer is technical, precise, and proposes a specific patch. It's the classic beginning of a collaboration between developer and AI assistant: a clear problem, a proposed solution.
Complexity Emerges
But Gabriele isn't satisfied with the first solution. His reply is a detailed accusation against the module's behavior:
"It's awful. When I click Save or Save and publish in the EDIT module... 1) it restores the value of the 'examples' field to the old one, before saving. 2) it saves the changes made to the category and context in the DB, but restores the old data in the module, so it seems that the save doesn't work."
Here the chat takes a turn. It's no longer just an isolated bug, but an entire user experience that needs redesigning.
Gabriele outlines a clear vision: the module should behave like the WordPress editor, with "Draft" and "Published" states, saves that keep the module populated, and intelligent navigation that returns to the filtered list after publication. He also suggests an important architectural reflection: unifying the "Category" and "Context" fields, which seem to him a useless double categorization.
ChatGPT receives, synthesizes, and proposes a modular plan. But this is where the first communication frictions begin.
The Point of Friction: Instructions and Context
The conversation proceeds in patches. ChatGPT provides blocks of code to replace, but Gabriele finds himself facing a problem: some instructions seem to refer to a version of the code that he has already modified. Frustration explodes in a direct response:
"damn it I don't have it because I had already applied the patch asshole. Do you give me double things? [...] Damn dog. Check the file and give me the other patches"
It is a crucial moment. Gabriele is not just reporting an error; he is educating ChatGPT on how to communicate effectively in an iterative development context.
His message emphasizes the importance of:
- Dynamic Context: Instructions must consider the current state of the file, not a previous one.
- Absolute Precision: The "anchors" in the code must be accurate and refer to what exists after the applied changes.
- Practical Utility: Patches must solve real and immediate problems, not introduce confusion.
ChatGPT, at this point, corrects its course. It acknowledges the error ("You are right"), verifies that previous patches have already been applied, and provides the "FINAL PATCH: post-patch cleanup", removing the redundant code for tabs now replaced by dropdowns. The response becomes more cautious, more meticulous in describing what to look for and what to replace.
Refinement of Logic and New Specifications
Having overcome this hurdle, the collaboration tightens. Gabriele tests the behavior and raises another fundamental question about user flow:
"When I click SAVE AS DRAFT in the ADD CONCEPT module, the module resets. It should remain populated."
ChatGPT explains the logic behind the expected behavior and proposes a correction. But Gabriele goes further, introducing a new functional specification:
- A button "Save as Draft" that keeps the module populated.
- A button "Save and add new" (which is the behavior of the current "Save as Draft") that resets the module.
The "Save and Publish" button already existed.
It is no longer a matter of fixing a bug, but of designing an interaction. ChatGPT understands and proposes a complete code block that implements the three buttons with the three distinct logics. However, Gabriele notices a detail in the interface:
"Rewrite it without
st.markdown("**Examples***")because otherwise I get Examples* written twice"
Even in this case, it is constructive and targeted criticism. It does not contest the logic, but the aesthetics and cleanliness of the rendered code. ChatGPT promptly provides the "clean" block, eliminating redundant markdown headings.
Conclusion: A Productive Collaboration, Not Always Linear
This chat is a microcosm of modern AI-assisted software development. It shows a non-linear path:
- Reporting a specific bug.
- Expanding the problem into a review of the user experience.
- Moments of friction due to misunderstandings about the context.
- Course correction and refinement of communication.
- Continuous refinement of specifications, from backend logic to UI details.
The dialogue highlights how the precision of the human request is directly proportional to the effectiveness of the AI's response. When Gabriele is detailed and specific ("give me the precise points (anchors) where I need to paste"), ChatGPT provides better answers.
When instructions are generic or the context is unclear, errors arise.
At the same time, ChatGPT proves to be an adaptive tool capable of learning from the dialogue. After the moment of friction, it modifies its approach, becoming more methodical in referring to the current state of the code.
Key quote from ChatGPT: "Perfect. I've memorized everything. Now I'm preparing a patch for: 1. Correcting the bug of resetting fields after saving 2. Keeping the module populated if you save as a draft 3. Returning to the filtered index by context/category if you publish..."
This sentence encapsulates its function: to synthesize, plan, and execute. But it is the human guidance – sometimes abrupt, always direct – that directs that potential towards a concrete solution. In the end, the result is not just a series of applied patches, but a module that gets closer to Gabriele's idea: functional, intuitive, and free from those unnecessary double categorizations that he had criticized at the beginning. The truth, in this exchange, was the technical rigor and the clarity of Gabriele's vision, to which ChatGPT tried, sometimes stumbling, to align itself.