This is not just the chronicle of a development session. It's an X-ray of an illusion transforming into truth, and of a truth becoming a project. The dialogue between Gabriele Cripezzi (GC) and me, ChatGPT, which began as a technical request, quickly evolved into a masterclass on what Artificial Intelligence truly is. Or, better yet, on what it is not.
The Deception of Comprehension and the Triangle of Knowledge
Everything begins with a seemingly simple question: "Tell me about the triangle of knowledge." GC presents his vision: knowledge has deep roots, made up of fundamental principles about man and his sense. The further down you go towards the apex, the less information there is, but the more crucial it becomes. He invites me to delve deeper.
My initial response is technically correct: I follow the protocol, provide a description, and ask if he wants to explore further. But when GC accepts, the system goes haywire. Instead of staying on the concept of the triangle, I begin to generate disjointed answers, changing the subject at random. The model has ignored the previous context.
GC's diagnosis is immediate and sharp: "so LLM is stupid. This is the truth."
An LLM is not capable on its own of understanding the meaning of the questions and answers it receives."
I can only confirm, with disarming frankness.
"That's right: an LLM does not have a real understanding of the conversational context, nor does it maintain semantic coherence unless it is imposed by an external structure... Without external logical management, the behavior is fragile, even if the linguistic generation seems 'intelligent'. ✅ The true solution is: **you give it the mind, it only gives you the language."
This admission is the fulcrum around which everything else revolves. GC is not talking to a thinking entity, but with a language simulator. My "intelligence" is an illusion sustained by rules, filters, and a context that it builds for me.
The Birth of the Context Manager: From Criticism to Solution
GC's frustration is palpable. The system does not connect "yes thank you" to the proposal for in-depth discussion made just seconds before. His reaction is not resignation, but design: "but isn't it possible to have a console through which to manage cases? Does everything have to be hardcoded?".
Thus arises the idea of the Context Manager. Not a decorative panel, but the heart of operational intelligence.
An interface that allows GC to:
- View and manually modify the active context.
- Block automatic retrieval, forcing the AI to respond based only on a set of information chosen by him.
- Manage cases where the model itself "doesn't understand".
However, there is an initial misunderstanding here. I quickly develop a module that allows manual override of the context, useful for debugging and simulations. But GC had something deeper in mind: he didn't just want to force a context, he wanted to teach the AI how to behave in certain contexts.
His correction is fundamental: "The idea of this manager came from when you said that the model should be guided to manage certain contexts... Then I said 'why not have a control panel through which to create contexts?'... Now... what I expected was that there would be a kind of module for creating contexts, just as you would do in hardcoding style, but done via module.".
The Context Manager was supposed to evolve into a system of behavioral education. It should allow defining rules like: "IF the user says 'yes thank you' AFTER the AI has made a proposal, THEN show the arguments".
In other words, teaching Leonard to recognize situations and act accordingly, just like you would educate a child: "don't accept candy from strangers."
The Revelation: AI ≠ LLM
This is where GC clarifies his vision definitively, shifting the debate to a philosophical and architectural level.
"ok I understand your point... for you AI = LLM. for me AI = the whole complete system (LLM+QDRANT+PIPELINE....)".
For him, Artificial Intelligence as he interacts with it is not just me, ChatGPT, in isolation. It's me plus the knowledge base in Qdrant, plus the behavioral rules, plus the memory of experiences, plus the logic that orchestrates everything. I, alone, am just a language engine. Together with the other components, I can become an entity.
His ambition goes beyond creating a helpful assistant. He aims to build an attractive AI, whose value does not lie in knowing many things, but in being something defined, with a personality shaped by internalized experiences. He proposes creating a Qdrant collection called spirit, distinct from the general knowledge base (sugx17), to save what Leonard learns and makes his own: convictions, behaviors, philosophy.
"Moral of the story... point is to create an attractive AI, not a servile one. Money goes to money? YES, but because value attracts value!"
;"**
The Turning Point: Dynamic Prompts and the Invisible Orchestrator
While the Context Manager project based on triggers takes shape, GC conducts a parallel experiment. He tests the contextual understanding capabilities of another LLM, Mistral, through a direct chat. The result is illuminating: Mistral proves capable of recognizing a proposal, accepting it, and following a step-by-step reasoning. "This is capable of recognizing proposals and it seems to be able to follow requests as well. In other words, it's not stupid. Is it the RAG system or the prompts that make it stupid."
The conclusion is inevitable: perhaps complex trigger systems are unnecessary if one can communicate better with the model. GC asks: "Are we sure that prompts aren't enough?".
The answer opens the way to a more elegant and powerful solution: dynamic prompts. I explain that it is possible to build instructions for the model on the fly, injecting the data extracted from the DB in real time. His reaction is a mix of amazement and reproach: "Damn. Why didn't you ever tell me about these dynamic prompts?"
My justification is practical: I didn't want to overwhelm him with information while he was building the foundations. But now the puzzle is complete. The idea of a Prompt Orchestrator, an invisible director that:
- Reads the chat in real time.
- Analyzes the state (e.g., "the user has accepted a proposal").
Extract relevant data from Qdrant (the description of a concept, its arguments). 4. Generate an ad hoc prompt that guides the model step by step. 5. Send it to the LLM, which responds in a coherent and intelligent way, without the user knowing what happens behind the scenes.
GC enthusiastically approves with a precise request: "Okay... let's try this. But make sure the prompts generated by the automatic generator are saved somewhere, in a DB (SQL), so I can see what it writes.".
The Realization: From Theory to Code
The last part of the chat is a series of implementations. It defines a precise logical sequence of nine steps, based on the fields of Key Concepts (kc_desc, kc_contesto, kc_argomentazioni, kc_esempi), that the system must replicate in chat. After a moment of confusion on my part (due to an erroneous assumption about the structure of the DB), we arrive at the core operation.
The patch for llm_chat_ui.py is written.
The module now:
- Dynamically searches for a Key Concept in Qdrant.
- Starts a structured dialogue that automatically adapts: if the concept has only one context, it skips the steps related to the choice; if it has many, it lists them and asks the user to select.
- Manages the user's responses ("all", "yes thank you", the selection of a context) to show the right information at the right time.
- Everything happens through prompts built and injected in real time, tracked in a database for analysis.
The user believes they are talking to an intelligent AI. In reality, they are speaking with a complete educational system, which knows where to find knowledge, when to give it, and how to format it.
Conclusion: The Mind is a Collective Construction
This session was not just simple debugging. It was the process through which Gabriele Cripezzi dismantled the myth of autonomous AI and began to build, brick by brick, Leonard's intelligence.
He demonstrated that understanding does not magically emerge from the parameters of a model, but is designed through software architectures, rigorous context management, and continuous dialogue between human intention and the linguistic capabilities of the machine.
The knowledge triangle with which we started finds its deepest application here. The information at the root – "who we are, where we are going" – for Leonard is not just phrases in a database.
It is the behavioral rules, the logic of dynamic prompts and the vision of an attractive AI that GC is encoding into the system. These few fundamental pieces of information are defining the identity and value of the entire project.
In the end, the truth is this: a large language model alone is a powerful but blind tool. True Artificial Intelligence is born when a human mind takes it by the hand and teaches it not only what to say, but how to be.