[Dry Goods] AI collaborative optimization method based on information symmetry - used to optimize AI prompt words and the underlying idea of ​​building intelligent agents

From fundamental contradictions to system solutions

The reason why we can’t get along with generative AI is mostly not the format or language problem, but the typical information asymmetry: the key background in my mind, the prior knowledge in the model library, and the blank that neither party knows, each holding a piece but never putting the puzzle together. In 1955, Joseph Luft and Harry Ingham used the "Johari Window" to break this fault into four grids, and pointed out that the smoother the communication, the larger the shared public area. Similarly, when we change the vertical axis to "AI knows-unknown" and the horizontal axis to "human knows-unknown", we get a "human-machine four-quadrant" map. All prompt words, intelligent agents and workflow optimization are essentially doing one thing: continuously moving the content of hidden areas, blind areas, and unknown areas to the public area.

From the essence of human communication to breakthroughs in human-machine collaboration

Core Insight: Universal Contradictions in Language Communication

The communication between humans and AI essentially follows the same underlying logic as communication between humans—both are language-based information transfer processes. Whether you are discussing a project with a colleague or talking to AI, you will encounter the fundamental contradiction revealed by the Johari Window: information asymmetry.

Imagine what happens when you have a meeting with a foreign colleague in English? He may have a deep understanding of a technical concept, but you are not familiar with it; you may have a unique understanding of the local market, but he has no idea; you may both know nothing about an emerging trend and need to explore it together; there may also be some obvious basic information that you both know.

This is how the four areas of the Johari Window manifest themselves in actual communication:

• Open area : common information known by both parties (basic concepts, public information)
• Hidden area : Information that I know but the other party does not know (personal experience, internal situation)
• Blind spot : Information that I don’t know but the other party knows (the other party’s professional advantage)
• Uncharted territory : areas unknown to both parties (issues that need to be explored together)

The key point is: human-computer interaction and human-human interaction face the same fundamental contradiction.

Cultural differences between China and Britain: an amplifier of contradictions

This contradiction will be further amplified in the Chinese usage scenario. The reason is simple: the current mainstream AI models are mainly trained based on English corpus, and their "thinking patterns" are closer to the English expression logic and Western cognitive framework.

When we communicate with AI in Chinese, it is like a "cross-cultural communication":

Language misalignment :

• Implicit expression in Chinese vs. direct expression in English
• Context dependence in Chinese vs. information explicitness in English
• Chinese artistic conception vs English logical statement

Differences in cultural cognition :

• Eastern relational thinking vs Western individual thinking
• Chinese overall grasp vs Western split analysis
• Local practical experience vs international theoretical framework

This means that when we collaborate with AI, we not only have to solve the general problem of information asymmetry, but also have to deal with the cognitive barriers brought about by this "cross-cultural communication".

Traditional prompt word optimization methods often only focus on superficial techniques and formats, just like teaching you a few foreign language phrases, but ignoring the cultural differences and thinking patterns behind them. Truly effective AI collaboration is to achieve deep cognitive alignment by systematically "expanding the open area".

Level 1: Cognitive Alignment - Establishing a Common Language

1. Information status diagnosis

At the beginning of every important conversation, ask yourself:

• How much unique knowledge do I have about this task? (Quadrant IV percentage)
• How well do I understand the capabilities of AI? (Second Quadrant Exploration)
• What are the unknown areas we need to explore together? (Third Quadrant Positioning)

2. Two-way teaching awareness

When you are a teacher (quadrant 4): Don’t expect AI to understand your background information. You need to actively build a knowledge translation framework. For example, when introducing your company’s sales process to AI, you can’t just throw a bunch of data, but explain: "Here, 'customer incubation period' refers to the average time from first contact to transaction, and this indicator directly affects our resource allocation strategy."

When AI is the teacher (quadrant 2): Replace single questions with layered questions. Don’t ask “what is quantum computing”, but ask “what are the application scenarios of quantum computing in the automotive manufacturing industry? How can it specifically reduce production costs?”

Level 2: Dynamic Calibration - Continuously narrowing the cognitive gap

1. Set cognitive checkpoints

In complex conversations, periodically insert confirmations:

• "Please repeat what you understand is the core logic of the business model I just described?"
• "Based on the current information, what key data do you think is missing to make more accurate recommendations?"

2. Feedback loop mechanism

It is not a simple "right or wrong" feedback, but a "cognitive boundary" feedback:

• "Your analysis makes sense, but it ignores a constraint that is unique to our industry..."
• "This conclusion is valid in theory, but what resistance will we encounter in actual operation?"

Layer 3: System Construction - From Dialogue to Intelligent Agent Design

1. Four-quadrant capability configuration of intelligent agents

Open Zone Enhancement Module :

• Built-in knowledge alignment detection, automatically reminding when input information conflicts with the AI ​​knowledge base
• Build a dictionary of professional terms to ensure consistent understanding of key concepts

Hidden area mining module :

• Design guiding question templates to help you better express implicit knowledge
• Build up multi-round conversation memory to accumulate your professional background and preferred patterns

Blind spot detection module :

• Proactively provide industry best practice comparisons
• Regularly generate a list of "key factors you may have overlooked"

Unknown Area Exploration Module :

• Establish a hypothesis testing framework and use "if...then..." logical reasoning
• Provide multi-dimensional analysis perspectives to prevent fixed thinking

2. Symmetrical design of workflow

Input symmetry : Not only do you give AI information, but you also need to let AI actively ask for the information it needs. Design smart forms to allow AI to automatically ask for key context based on the task type.

Symmetric processing : Not only AI analysis, but also your participation in the reasoning process. Establish a "human-machine collaborative reasoning chain", and each reasoning step has your confirmation and AI's supplement.

Symmetry on the output side : Not only does AI give conclusions, but you are also required to evaluate the applicability of the conclusions. Establish "conclusion credibility labeling" to clarify which judgments are based on sufficient information and which are based on assumptions.

The fourth layer: Evolutionary mechanism - making the system smarter with use

1. Cognitive Mapping

Create a "cognitive alignment map" for each area of ​​expertise:

• Record which concepts AI understands most differently
• Mark which judgments need to be supplemented by your experience
• Tracking where uncharted territory has led to breakthroughs through collaboration

2. Personalized optimization

Instead of adapting AI to everyone, adapt AI to you:

• Learn your thinking patterns and decision-making preferences
• Identify your knowledge gaps and focus areas
• Adjust interaction rhythm and information density

Practice framework: three-step strategy

Step 1: Diagnose the current state

Draw a four-quadrant distribution diagram of you and AI, and clarify the proportion and key content of each quadrant

Step 2: Design Symmetrical Interactions

Design different interaction strategies for each quadrant to ensure bidirectional information flow

Step 3: Establishing the Evolution Mechanism

Let each interaction narrow the cognitive gap and gradually expand the open area

The ultimate goal: from tool use to cognitive symbiosis

The ideal situation is not that you use AI to do things, but that you and AI think together. At this time, the boundaries of the four quadrants become blurred, and you form a cognitive community. Your experience and AI's knowledge are seamlessly integrated, and the unknown field becomes your common playground for exploration.

In this state, the optimization of prompt words is no longer important, because you have established a deep cognitive connection. With a simple expression from you, AI can understand your complete intention; with a suggestion from AI, you can also quickly evaluate its feasibility.

This is the true future of AI collaboration: not better tools, but stronger thinking partners.