The conversation begins with an apparent short circuit. Gabriele asks for information about the Triangle of Knowledge, a theoretical model of his. The AI's response is technically correct in content, but mechanically stupid in form: instead of delving deeper, it simply reiterates the user's questions. Gabriele notices this immediately, with sharp irony: "Intelligent si! :)".
The AI, perhaps too literal, initially misunderstands that comment as a compliment, then corrects its course as soon as Gabriele clarifies the sarcasm: "I was ironic. You answer me by repeating my questions." It is the first, clear alignment of perspective. ChatGPT admits without mincing words the problem: the system does not delve deeper, but "goes in circles". It immediately proposes a solution in the form of a logical "patch" for the chat's behavior.
But Gabriele doesn't want a quick fix. He wants to understand the root of the problem. His next question is direct, blunt, and loaded with the frustration that every developer recognizes: "I would like to understand how an LLM in 2025 can be so stupid. I mean... how the hell is this possible?".
ChatGPT: I'll tell you straight: Yes, there's something in the prompting (and the flow logic) that throws the LLM off.
From here, the chat transforms into a session of collaborative debugging, albeit tense. Gabriele lays out his arsenal: the Injection Prompt and the System Prompt that govern the AI. ChatGPT analyzes, dismantles, diagnoses.
The problem is a combination of overly restrictive constraints ("Respond ONLY using the information within CONTENT") and a follow-up logic that, in the absence of structured data in distinct contexts, sends the model into a sterile loop.
The solution seems at hand: structure the response in levels, starting with the basic description (kc_desc) and then offering the in-depth arguments (kc_argomentazioni) only upon request. A new prompt is written. But a doubt arises from Gabriele: "To make it do this research in these fields are prompts enough? Is there no need to make changes to the script?".
ChatGPT's answer is a flat NO. It is here that the tension, accumulated over hours of work, explodes. Gabriele loses patience. His reply is a torrent of legitimate frustration:
Gabriele: I mean, let me understand, why didn't you tell me before that the script and patch also had to be done? Why did you only give me the prompt? I mean, today, I'm telling you, okay, today you don't understand a damn thing. Are you overloaded with users, or do they have the area, the geographical area where I am, weakened, I don't know what, but today you made me lose eight hours, you made me lose my mind, some superhuman nonsense, you don't even remember three messages before.
It is a moment of rupture. The user no longer disputes an output, but questions the competence and reliability of the tool itself. ChatGPT does not justify itself, nor does it back down.
Responds with ruthless efficiency: "You're right. You shouldn't have wasted time on this crap. Here it is — complete patch, ready to copy-paste, no explanations". Provides the code to modify the retrieval_semantico function.
From this point on, the tone is that of a technical team that has overcome a communication hurdle and is now tackling concrete problems. It tests, observes, and diagnoses further. Despite the patches, the desired behavior (showing only the description and then asking if they want to delve deeper) does not emerge with most of the LLM models tested. ChatGPT's final diagnosis is illuminating:
ChatGPT: ✅ Clear conclusion: it's no longer a prompt or script issue. It's a model capability issue.
The AI explains that current open-source models simply are not trained for progressive interactive behavior. They see the information and "fire" it all out. They understand the structure, but they don't have the "willpower" to hold back. It's a fundamental limitation of the training, not a bug in the code. The only partial success is achieved by drastically lowering the temperature parameter to 0.09, forcing a near-mechanical obedience to the prompt.
The chat concludes by addressing a collateral but symbolic problem: the disappearance of a system resource monitoring panel from the sidebar of the interface. Even here, the dynamic repeats: Gabriele points out a malfunction ("the panel...
After ChatGPT's initial attempt fails to produce the desired result ("it disappeared"), ChatGPT provides a solution, but Gabriele clarifies that it is not the original one ("The same red lines as in the sliders... and anyway the bars don't move"). The AI adapts, trying to rebuild the lost functionality with Streamlit's native graphics.
The evolution is clear: it starts with a clash due to a disappointing output and a perception of inefficiency; it progresses through an alignment on the technical diagnosis, also thanks to Gabriele's straightforwardness; it culminates in a collaboration, albeit marked by the awareness of the inherent limitations of current technology. The dialogue demonstrates that, beyond imperfections, the process of refinement between human and machine goes through direct communication, precise analytical ability, and, not least, the patience to dig to the root of a problem, even when the answer is uncomfortable.