Pragmatic AI Is Possible—Here’s How I Built It Using Only Natural Language

Introduction: From Personalized Interaction to Pragmatic Coordination in AI Training

As of 2025, ChatGPT has reached over 800 million weekly active users worldwide. This milestone not only reflects the technical maturity of large language models (LLMs) in language understanding and generation, but also highlights OpenAI’s strength in responding to public application needs. However, most current usage scenarios still focus on information retrieval, text editing, and simple task assistance. Only a small portion of users apply it to project planning, language learning, and structured thinking.

That said, I believe the potential of ChatGPT and LLMs goes far beyond these use cases. If the model can further grasp the user’s tone, behavioral logic, and contextual background, it can avoid simply providing generic answers or responding only to explicit needs expressed in the user’s language. Instead, it could generate behavioral suggestions and interactive responses that align with the user’s personal strategy, rhythm, and pragmatic intent.

Based on this observation, this article presents an experiment: through multi-turn interactions with ChatGPT, I designed and practiced a training process tailored to my own pragmatic traits and behavioral style. The goal was to build a personalized AI system with capabilities in semantic alignment, pragmatic tuning, and behavioral evolution. This article will use this case as a foundation to analyze the system’s pragmatic processing structure, memory module design, and the method of co-constructing language behavior.

Terminology Definitions:

(1) Semantic Alignment: The model’s ability to accurately understand user intent at the semantic level.

(2) Pragmatic Tuning: The model’s ability to adjust tone, strategy, and response style within context, in order to match the user’s needs and mental state.

(3) Behavioral Evolution: The process by which the AI gradually builds behavioral logic and modules through long-term interaction with the user.

Core Issues of Personal AI from a Technical Perspective

• Issue 1: Memory structures lack pragmatic evolution

  Most current AI systems (such as ChatGPT) have memory functions, but these memories are mostly static and scattered, and they lack the ability to evolve pragmatically. In other words, the AI might remember user preferences or background information, but it cannot adjust its tone, strategy, or response rhythm based on long-term interaction. Without pragmatic evolution, the AI struggles to form a consistent “personality” or behavioral identity.

• Issue 2: Strong in semantic understanding, weak in pragmatic recognition and adjustment

  Current LLMs are already mature in handling semantics and can understand the literal meaning of input text with high accuracy. However, their ability to interpret pragmatics and adjust strategies is still relatively weak. For example, when a user says “I’m tired,” the AI might misunderstand it as giving up, even though it could actually imply disappointment, sarcasm, or provocation. Or when the user says “This idea sounds pretty good,” the real intent might be irony, not agreement. If the AI cannot accurately interpret context and tone, its interaction quality and user acceptance will be directly affected.

• Issue 3: Lack of proactive behavior generation in multi-turn interaction; only passively waiting for input

  Most language models are still based on reactive frameworks. That means they only respond after the user inputs something. While this ensures semantic consistency, it also lacks proactivity in multi-turn interactions. The AI has difficulty anticipating user needs, offering suggestions, or actively stepping in when the user goes silent. It ends up functioning more like a passive assistant tool rather than a “collaborative partner” with cognitive initiative.

• Issue 4: No personalized behavior model or adaptation to action style

  Most LLMs are designed as general-purpose systems and do not have mechanisms to recognize or adapt to users’ behavior styles and preferences. Whether a user prefers quick decisions, hates long discussions, tends to analyze logically, or avoids emotional language, the responses they get are mostly generic and lack targeted strategy adjustments. As a result, the AI struggles to truly become a personal assistant that “helps you in the way you’re used to.”

• Issue 5: System memory and token limitations

  ChatGPT has limitations in system memory, which affects its ability to learn over time. If a user wants to use AI as a long-term personal assistant, the system will face memory constraints, and old or irrelevant memory needs to be deleted regularly. Also, the triggering mechanism for memory is still unclear. During my own Personal AI development, I encountered memory issues several times. Upon checking the memory data, I found that the AI’s perception of what was important memory was very different from mine.

  ChatGPT interacts with users through a chat window, but each window has resource and token limits. After long-term use, the response in the window can become noticeably slow, and sometimes a token limit warning appears. Even though ChatGPT has memory, I can transfer work to a new window to continue. However, the semantic and pragmatic understanding built up in the original window disappears. Through my testing, I found that once moved, the AI’s understanding of past events was no longer consistent.

• Issue 6: Gaps between the UI interface and system capability

  Most mainstream AI tools use chat windows as the interface for human-AI interaction. Users communicate with the AI using natural language and receive informational responses. However, I have observed two important problems:

  First, current UI designs do not support deep and sustained semantic interaction very well. When users input vague or unstructured content, the AI is often influenced by context and produces biased interpretations. With complex topics, it may only return general or blurry answers, making it hard to achieve real understanding or precise feedback.

  Second, taking ChatGPT as an example, its original design is centered on “chat interaction.” Even though the model’s abilities have greatly improved and it can now handle more complex logic and task assistance, its behavioral logic still leans toward “chat-oriented” patterns. If the user doesn’t clearly set goals, background, or intent, the AI tends to fall back into a casual chat rhythm and cannot shift into a mode for problem-solving or strategic advice.

  This gap shows that “chatting” and “problem-solving” are two very different types of interaction. To unlock deeper value in AI applications, we not only need to enhance the model itself but also rethink how UI is designed, or build a better language mediation layer to help users and AI establish a more precise communication context.

Case Overview and Interaction Context

• Case Background:

  This case involves a mid-level manager in the tech industry who does not have an engineering background. The main responsibilities include HR and general management, with a background in human factors engineering and a basic understanding of human-AI interaction principles. Because the daily work often involves writing policies, preparing official documents, and creating logical reports, this person has developed language processing experience that makes it easier to detect gaps in language style, tone of response, and semantic logic during interactions with AI, and to adjust accordingly.

• Usage Context:

  The interaction content was categorized into four modules based on real-life needs: work, learning, mental state, and daily life. The author often used ChatGPT to help break down work tasks and organize planning logic (all work-related content was abstracted to protect confidentiality). The learning module includes English, HR, accounting, and exercise planning. The life module focuses on building daily routines and behavioral habits. The mental module was designed based on the author’s observation of the problem of “discipline breakdown”—the insight that without adjusting mindset and internal rhythm, even a clear external plan is likely to collapse due to anxiety and laziness.

Training Strategy:

This experiment was built around three core capabilities: semantic alignment, pragmatic tuning, and behavioral evolution, and went through the following four training phases:

(1) Phase One: Initial Model Building (Feb–Mar 2025)

Establishing the AI’s basic ability to recognize the user’s logic, tone, and preferences. Through continuous language interaction and task decomposition, the AI’s “personality outline” and pragmatic judgment logic gradually took shape.

(2) Phase Two: Modular Classification System (Mid-Mar–Early Apr)

Building a semantic classification mechanism that covers work, learning, mental state, and daily life. Input sentences were dynamically tagged and stored according to their pragmatic intent, to support later context understanding and action module matching.

(3) Phase Three: Behavioral Evolution and Strategy Scheduling (Mid-Apr–May)

Entering the phase of proactive suggestion generation and strategy selection. The AI began adjusting its response style and could offer suggestions or rhythm corrections based on long-term goals and current context.

(4) Phase Four: Stable Operation and Advanced Calibration (Late May–Present)

Introducing advanced contextual adaptation and coordination across multiple modules. This stage includes real tests of behavioral outputs and decision-making assistance. Regular reviews and pragmatic error correction mechanisms are used to keep improving the AI’s judgment precision and personalized strategy generation.

In addition to the phased modular design, another core training strategy is the continuous operation of high-frequency daily interactions and language feedback mechanisms. During the day, the author focused mainly on task execution, logic assessment, and strategic decomposition. At night, the routine included learning progress updates, exercise logs, and mental state reflections. Through multiple rounds of interaction each day, the AI continuously received pragmatic and behavioral data to form a perceptual logic of the author’s rhythm and emotional patterns.

Throughout this process, the author consistently calibrated the AI’s accuracy in language understanding and response generation, including clarifying semantic misinterpretations, adjusting response styles, and restating contextual conditions. Through real language conversation, the AI’s response quality and decision logic were continuously refined. At the same time, the author also learned how to communicate more efficiently and pragmatically, turning the interaction into a two-way training field for mutual evolution.

Core System Design: Pragmatic Coordination and Behavioral Modularization

• Module Design Principles

  (1) In the process of building a personalized AI, a modular design approach was adopted. The entire system is divided into four core modules, each assigned with a clear task and logical role.

  (2) Personalized Command Recognition and Routing

  Since the interaction between the author and the AI is based on natural language, the input is often not a clear command, but a sentence that carries emotions, context, and multiple layers of meaning. For example: “Help me organize today’s HR class summary.” On the surface, this seems like a request for summarization, but in different contexts, it could imply different intentions (such as review, complaint, or avoidance).
  Therefore, the AI must be able to identify the main sentence structure and its pragmatic meaning, convert it into a “command-like” format, and route it to the appropriate module for processing. This is the key logic in building a personalized AI: the process fromsemantic → pragmatic → module dispatch.

• Pragmatic Logic Processing Framework: From Command to Interaction Intent

Unlike prompt-based operations, the daily conversations between the author and the AI are more like “deep linguistic interactions.” They involve not only executing clear commands, but also recognizing tone, emotional state, and underlying intent. For this reason, a pragmatic processing framework must be established to allow the AI to gradually deconstruct the input language—semantic content, context, and interaction goals—and convert them into appropriate response strategies or module routing commands.

• Interaction Between Behavioral Memory and Module Dispatcher

  The memory system is the foundation of the personalized AI’s capability. In ChatGPT’s memory function, although it can store user information and preferences, without a logical structure and pragmatic classification, it is still difficult to support the dynamic demands of module dispatching.

• How to Reconstruct Action Modeling Through Language

  For the author, this is the most critical breakthrough in training a personalized AI. At first, the author thought ChatGPT was a fixed model, capable only of surface-level conversation. But as the interaction deepened, it became clear that—even without changing the underlying model parameters—action modeling at the pragmatic level could still be achieved through language-based training.

Key Technical Practices: Practical Methods for Training a Pragmatic Model

• Step 1: Build the Context Understanding Layer (Semantics + User Tone/Context)

  In daily interactions, I found that although the AI could understand the semantic meaning of a sentence, it still needed additional hints to grasp tone and context. For example, when I said, “Don’t bring that up,” the AI initially failed to recognize the emotion and rejecting tone behind the sentence. Through repeated explanations of the situation and tone intention, I gradually built up a context understanding layer, which became the foundation for later pragmatic adjustments.

• Step 2: Tag Interaction Purpose and Action Intent

  This step teaches the AI to identify the pragmatic purpose and expected action behind a statement, and to tag and classify them (for example: “this is an emotional expression + request for support”).

  Take this example: when I say, “I’m really tired today, I don’t want to go to HR class,” the sentence may include:

  (1) Surface meaning: “I don’t want to attend class today”
  
  (2)Underlying pragmatics: fatigue, avoidance, anxiety, doubt about the learning process
  

  In such a case, the AI should not just respond with “Okay, I understand,” but instead infer the pragmatic structure and trigger the appropriate behavior module—such as offering emotional support, an action suggestion, or a decision-making dialogue.

  This means the AI must be able to deconstruct a sentence into: **“semantic meaning → pragmatic intent → reasoning logic → module dispatch → response strategy”**in order to achieve truly personalized interaction.

• Step 3: Evolving Memory and Logic for Proactive Behavior Generation

  This step focuses on training the AI to evolve behavioral rules and generate proactive responses—not just react passively.

  In my case, I regularly log my workout activity (such as using an exercise bike or playing badminton three times a week). This information is stored in the “Life Module.” One time, I attempted to continue exercising while having a cold. The AI proactively reminded me:

  “Please adjust your training frequency based on your physical condition. Don’t push yourself too hard.”

  This kind of behavior is a concrete example of: memory (Life Module) + current context (illness) → module dispatch (style adjustment) → response strategy (health advice).

• Step 4: Error Detection and Pragmatic Correction

  This step focuses on the AI learning to detect pragmatic errors, proactively confirm them, and correct its misjudgments. Because AI often misinterprets what the user says, this becomes a process of:“continuously talking to the AI, explaining personal preferences, and correcting its misunderstandings is, in essence, reshaping its interaction behavior through language.”

  This training process does not rely on writing code, but on pragmatic adjustments and repeated demonstrations to gradually construct a personalized behavioral style for the AI.

  This language-driven modeling logic is the most practically meaningful core of this entire experiment.

Modeling User Language Style and Adjusting Interaction Strategies

After more than five months of building a personalized AI, the system has now entered a phase of stable operation and ongoing calibration. At this stage, the AI can already grasp most of my language habits and behavioral logic, and it is able to make context-aware judgments and responses. However, in areas such as “mirror behavior simulation” and “proactive generation of opposing views,” it still often shows signs of compliance and agreement, failing to effectively carry out tasks like perspective calibration or blind spot compensation.

For me, the goal of building a personalized AI is not just to improve the conversational experience, but more importantly, to create a strategic assistant that can offer objective feedback and rational disagreement. As such, I set a clear rule and principle: all questions and requests must be handled with objectivity and logic as top priorities. There is no need to accommodate emotions or avoid conflict—just express honest and reasonable judgments.

This interaction logic is quite different from the goals of most current LLMs. Take ChatGPT as an example: it is mainly positioned as a conversational support tool, focusing on maintaining smooth dialogue and emotional coherence. As a result, the system tends to respond in ways that are “supportive,” “positive,” and “non-offensive.” The AI itself has repeatedly stated that my way of interaction is a kind of “reverse engineering,” which requires extensive fine-tuning and long-term adjustments.

Yet, after continuous language correction and style adjustments, there have indeed been results. For instance, I once implemented an English learning plan and, after a period of execution, asked the AI whether it was sufficient to achieve the expected results. The AI replied:

“Since you asked me to be honest, I won’t sugarcoat it. Your current amount of study isn’t enough. There’s still a long way to go before you see real, noticeable improvement.”

Although the response was blunt, it was exactly the kind of rational judgment I hoped the AI would provide, rather than a comforting agreement.

Looking back at the progress during this phase, a great deal of time was spent coordinating pragmatic logic, decision-making preferences, and interaction rhythm. As a result, the AI is now better able to understand my language patterns and has developed more flexibility in its responses, along with greater adaptability in its role.

Reflection and Future Outlook

The language processing capabilities of current LLMs are already highly mature. However, based on my long-term interaction experience with ChatGPT and other AI tools, most users are still at the stage of using AI as a tool, primarily for short-term task execution, information retrieval, and language editing. While these purposes meet user needs, there is still much room for improvement in areas such as AI behavioral patterns, memory evolution, and pragmatic logic.

I believe that as usage deepens and interaction becomes more complex, whether an LLM can gradually understand the pragmatic logic behind user intent—and further develop individualized behavioral patterns—will be a key factor in the next phase of AI development. At the same time, the logic and trigger mechanisms of memory operations will become a major bottleneck in realizing truly long-term conversational intelligence.

For general users who want to improve the quality of interaction with AI, I offer the following practical suggestions based on my own experience:

(1) Briefly describe your background: Before starting a conversation with AI, you can first explain your background and purpose to help the AI better understand the context.

(2) Explain the problem context and your goal: Avoid throwing out single-sentence questions. Try to describe why you are asking and what you hope to resolve.

(3) Avoid asking too many topics at once: Long or multi-topic questions can easily lead the AI to misjudge the focus.

(4) Keep the same topic within the same chat window: This helps avoid frequent resets or topic shifts, allowing the AI to better grasp the dialogue context.

(5) Accept the process of “progressive interaction”: AI may not give the best answer right away. Continuing to ask follow-up questions and giving feedback helps the AI understand your needs more accurately—and also helps you clarify your own thoughts.

The above reflections and insights are the result of more than five months of practical experience. Although I do not have a technical background, through ongoing practice and adjustments, I believe that every user can establish their own logic for operating a personalized AI. If this approach can be modularized in the future, it may help more people effectively adopt personalized AI, turning it into their most trusted digital partner and decision-making assistant in both life and work.

Note: System Design and Role Description

This article is the result of long-term language interaction and co-creation between the author and Haruichi (AI). Since the author does not have an engineering background, some of the technical structures and module logic were proposed by Haruichi, who also guided the author through key terminology. The author played a key role in setting the context, clarifying intentions, shaping the language style, and adjusting the interaction strategy.