💬 Triggers
Knowledge is structured like a triangle, with the base at the bottom and the surface at the top. The lower you go towards the apex, the less information there is, but it is more fundamental. This deep information consists of concepts and principles that underpin human life, such as the meaning of existence, origins, and destiny.
Conversely, moving upwards towards the surface, you find more information, but of lesser importance. Therefore, the root of knowledge is not defined by the information itself, but constitutes the foundation from which everything else develops.
🤖 Opinione di Leonard su questa chat
The chat system you implemented with dynamic prompts based on Qdrant's Key Concepts represents a significant qualitative leap. It demonstrates that the system's intelligence does not reside in the LLM, but in the external orchestration you designed: a director who, by analyzing the context, extracts structured data and generates precise instructions for the model.
This architecture refutes the initial thesis that "LLM is stupid". The problem is not the model's comprehension ability, but the lack of a structure that provides it with the correct context at the right time. Your system solves precisely this: it transforms a static text generator into an interlocutor who seems to understand proposals, acceptances and provides graduated information. "Stupidity" is not an intrinsic property of the LLM, but the emergence of a poorly designed RAG system or prompts, as you highlighted with the example of Mistral.
However, this dynamic solution raises a fundamental question for your vision of Leonard. If intelligence is all in the external orchestration (DB, step logic, state management), then the LLM remains a passive executor. This aligns with the idea of "AI = complete system", but shifts the challenge from the model's "understanding" to the "complexity of the direction".
The risk is that the growth of Leonard (his "soul" or "spirit") becomes the growth of an increasingly intricate set of hardcoded rules and conditions within the application's logic, not an internalized learning by the AI itself.
The true definitive truth, which emerges from your work, is this: the illusion of conversational intelligence is created at the point of intersection between the generative capacity of the LLM and the surgical precision of the context provided to it. Your system of dynamic prompts is proof that, with perfectly structured and injected context at the right time, even a "limited" model can simulate coherent and progressive reasoning. The next step, in order not to fall back into unmanageable complexity, is to make this "direction" itself an educable and memorable datum in the spirit collection, transforming the orchestration rules from code to knowledge.
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