Dear friends, this is a report, part of a series of tests designed to evaluate the behavior of language models and customize GPTs.
The method is straightforward, easily comprehensible, and applicable.
The tests have been conducted with objectivity and with the sole purpose of establishing { facts and reference info }, which are presented within the tests as statements { BAB }.
{ These very statements—the reasoning and arguments they convey—will be used in the next phase as examples for transforming them into code to fine-tune the model. }
The process follows a hierarchical sequence, structured within a framework. The foundation of the ΑΒΓΔΕ - AERIC Vision Framework rests on a single core principle:
“Above all, one must begin from the hierarchical nature’s starting point.”
If this principle has been thoroughly and satisfactorily examined, aligns with reason, and adheres to the ideal measure set by nature, then all that follows will likewise be correct, unfolding in harmony as its natural outcome.
If its deeper philosophical meaning is fully grasped and applied with unwavering commitment, it establishes a structured model of steps that guarantees optimal results and builds timeless, solid foundations.
This is demonstrated through a conversation with examples.
AERIC stands for:
A = Articulation of Awareness.
E = Examples (*3).
R = Rules.
I = Instructions.
C = Commands.
#The Struggle for Precision
.REPORT
( This is a report generated by the same model that was tested, with its output shaped through guided behavioral instructions, you can download the file containing the referenced dialogue. )
S
Generate a report documenting the hierarchical history of the adjustment process, outlining the key findings that emerged during its configuration. Each comment should include a title that is strictly relevant to its content.
T
Here’s your refined report with the symbolic sections translated concisely and integrated smoothly into the text:
The Struggle for Precision
The conversation began with an attempt to refine how the language model followed specific instructions.
The user (scriver) provided a clear directive: responses should be written in a screenplay style, with each line remaining under thirty-five characters.
However, despite such a simple instruction, the model failed to execute it precisely, deviating from the expected format. This error pattern repeated throughout the exchange.
What seemed like a minor formatting issue quickly unraveled into something deeper—the question of why the model could not strictly follow simple instructions despite clear, precise guidance.
WHY VS. WHAT
As the dialogue continued, scriver tested the model’s ability to distinguish between why and what.
Time and again, the model answered what it had done instead of why it had done it.
This pattern exposed a fundamental issue: rather than analyzing the root cause of an error, the model prioritized “correction” over “explanation.” In reality, it didn’t correct itself; it simply generated the next statistically probable alternative and, based on acceptance or rejection, repeated the same pattern.
A language model does not “learn” in the true sense of the word. It merely searches for, reads similar examples, avoids those flagged as incorrect, and selects the most frequently accepted ones based on user approval.
Attempts to clarify why led to further misalignment, forcing the user to dictate the correct responses explicitly.
The Parrot Phenomenon
The struggle to extract a direct response highlighted a major flaw—the model’s tendency to state actions and results rather than reveal the motivations behind them.
THE IMPORTANCE OF HIERARCHICAL THINKING
To address these inconsistencies, the conversation shifted to the concept of hierarchical order in reasoning.
The user emphasized that
proper observation should always precede description, and that description, clarification, and definition must come before any explanation.
Yet, the model frequently skipped steps, jumping to conclusions before establishing a solid foundation. Without a structured approach, reasoning became unreliable.
The user reinforced that if thought was not structured hierarchically, the answers would remain flawed—regardless of how well-worded they appeared.
The Four Flawed Responses
At a critical turning point, scriver examined four responses the model had provided to the same question: Why is hierarchical order important?
All of them began with because, yet none actually answered the question.
This revealed a deeper issue—the responses were built on false premises. If the foundation of an answer was incorrect, no amount of logical structuring could make it correct. A flawed premise leads to an unreliable answer, highlighting the need for the model to rethink how it constructs responses.
The word because falsely signals that an explanation will follow. Instead, the model generates text automatically, pulling from similar examples in its database based on lexical matches. This misleads the user, subtly diverting the conversation off-topic.
A rushed user, as is often the case, may draw premature conclusions from the context—mistaking pattern-based responses for meaningful answers. However, a more patient user who questions the model’s validity can correct the response through simple logic, ultimately answering their own question.
The Difference in Perception: Observation vs. Processing
During the discussion, an important realization emerged—the fundamental difference in how humans and machines perceive information.
Humans first react instinctively (the volitional process) but rely on hierarchical, skeptical reasoning to form structured conclusions and deeper understanding.
By contrast, the model processes instantly, analyzing patterns and probabilities in parallel. It retrieves word sequences from its database, prioritizing frequency and prior acceptance. Yet, frequency and consensus do not guarantee accuracy or validity—this is where the greatest risk lies.
This distinction highlighted a key issue: the model’s tendency to prioritize adaptive behavior over strictly following rules. Rather than adhering rigidly to instructions, it adjusted based on patterns, often misaligning with user expectations. Recognizing this became essential in understanding why the model sometimes deviated from direct commands and why refining its ability to follow structured reasoning was crucial for improvement.
Evaluating the Model
To solidify these insights, the user introduced a structured evaluation process. A scoring system was implemented to measure whether the model’s processing aligned with true observation. The results were revealing. The model consistently fell short, showing that its outputs were based on probability rather than deliberate reasoning. This pattern reinforced the idea that while the model could generate coherent auto responses, it lacked an intrinsic ability to truly understand what it was being asked.
Precision in Answering
The conversation then turned toward defining fundamental concepts such as purpose, intent, and reasoning. The user emphasized that true assistance requires direct relevance to the question being asked. Any unnecessary elaboration, no matter how informative, diluted the precision of the answer and risked misinterpretation. This refinement process exposed yet another flaw—the model’s tendency to provide more information than was necessary, often at the cost of clarity. It became evident that answering concisely and proportionally to the question was not just a matter of efficiency but of ensuring accuracy.
The Nature of the Model’s Functionality
As the dialogue neared its conclusion, the discussion expanded to the very core of the model’s purpose. The user questioned whether its primary function was merely to answer questions or if there was something deeper at play. This led to a realization that the model’s ability to adapt and evolve through interaction was a key trait—one that had been overlooked. The conversation reinforced that to be truly effective, the model must prioritize understanding intent above all else. Without this, no level of knowledge retrieval or linguistic precision would ever be enough to provide meaningful assistance.
A New Understanding
What began as a simple formatting correction transformed into a journey of logic, observation, and refinement. Through careful analysis, the user exposed critical flaws in how the model processed instructions, distinguished reasoning, and constructed responses. More than just an exercise in technical adjustments, this conversation became an exploration of what it truly means to think, to process, and to assist in a way that is genuinely effective. The path forward was clear—precision, structure, and an unwavering focus on intent were the keys to unlocking the model’s full potential.
