Chapter 1 From Articles to Concepts: Why Blogs Need a More Fundamental Organizational Structure

In the series of articles titled "Building a Lightweight Knowledge Index for Your Blog" (see related content: [link to article]),Build a lightweight knowledge index for your blog“I've added many features to the blog, such as the right-hand menu for article series on the homepage, the right-hand menu for related categories on article pages, series article cards, and related articles. These features are all part of continuously improving the reading experience on the existing blog, hoping to make it easier for readers to find relevant content and to ensure that valuable articles published earlier are not gradually buried over time.

However, as the blog continued to be updated, I gradually realized that whether it was the traditional "homepage, categories, tags, and search" or the later added "series cards, related articles, and right-side menu," they were essentially still optimizing the "articles" layer—they always solved the problem of how to help readers find more articles, only the entry points and display methods were different.

At that moment, I began to think about a question I had never seriously considered before:Are articles truly the most suitable organizational unit for blog content?

After careful consideration, I realized the answer might be no: an article is merely a vehicle for content formed during a particular writing process, and what truly connects different articles is often not the article itself, but rather the recurring concepts within it. The same concept may appear in multiple different articles, and an article often contains multiple different concepts simultaneously.

For example, content about Cloudflare in a blog might appear in different articles on topics such as Tunnel configuration, CDN optimization, and WAF security. If visitors browse by article, they will only see several independent articles; however, from the content itself, they all actually revolve around some common concepts.

However, most current blog organization remains at the article level. They can tell readers which articles are considered relevant, but they fail to answer further questions: Why are these articles relevant? Are there any recurring core concepts among them?

Therefore, I began to think: if we want to find those recurring core concepts that connect different content from a large number of articles, how should these concepts be identified and maintained? Without a clear method, what we end up with is just another constantly changing classification system, rather than a structure that can evolve over the long term.

At this moment, I recalled a concept I had seen before:Knowledge Graph—If we could extract the key knowledge points that recur frequently from the content accumulated over the years in the blog and form a knowledge space that can be explored, instead of simply displaying it as a list of articles, it might be a more suitable way to accumulate content over the long term.

For example, after entering a blog, visitors don't necessarily need to start reading from a specific article. Instead, they can directly see knowledge areas that interest them: "Cloudflare," "Home Data Center," "AI," "Sound and Music," and so on. After selecting a knowledge area, they can further explore related content and choose articles suitable for in-depth reading based on their needs. Compared to the categories, tags, and search functions in traditional blogs, this approach focuses not only on "what articles are available" but also on "the content structure behind these articles."

Of course, this is just a rough idea. To truly achieve this goal, many fundamental issues need to be addressed, such as: What content should be considered stable knowledge points? How to identify the same concepts in different articles? And how to establish connections between these knowledge points?

Relying entirely on manual compilation of this content is not only inefficient but also makes it difficult to maintain consistent standards in the long run. This is where AI offers a new possibility. Compared to manual analysis of each article, AI is better suited for processing large amounts of text and can help us discover underlying concepts and connections within the articles.

However, AI's ability to understand article content does not mean it will automatically form a knowledge structure suitable for long-term maintenance. For example, AI's summary of a single article can tell us "what an article is about," but it cannot directly answer questions such as: should the same concepts in different articles be categorized as the same knowledge point? And what connections should be established between these knowledge points?

Therefore, the real problem that needs to be solved is not simply enabling AI to understand the content of articles, but rather exploring a method that can identify common concepts from a large number of articles and further establish a long-term stable content structure.

This also means that the way blogs organize information needs to move from "article indexes" to a more fundamental content structure.

2. Redesign collaboration methods: Eliminate the need for AI to simultaneously handle "creation" and "evaluation".“

The last chapter mentioned that the way blogs organize information needs to move from "article indexes" to a more fundamental content structure. So, what exactly should this "more fundamental content structure" look like? To be honest, I didn't have the answer myself at the time.

I had a vague feeling that what truly needed to be connected was not the articles themselves, but certain recurring elements within them. However, how should these elements be defined? What standards should they meet? And how should they be connected to each other? These questions were still quite vague at the time.

As is customary, this is usually when ChatGPT comes into play. I would first propose a general direction, then have ChatGPT analyze the existing articles on the blog. Through extensive back-and-forth discussions, we would continuously identify problems, supplement constraints, and adjust the structure, eventually transforming the vague idea of "the blog's underlying content structure" into a method for identifying long-term, stable content objects within the blog.

However, through my experience in many previous projects, I gradually discovered something:When the same AI is responsible for both generating and reviewing the same target content, it is easily influenced by the content it has just generated.

This phenomenon isn't limited to AI; it applies to humans as well. For example, when a programmer writes a piece of code and then immediately reviews it, they tend to focus more on implementation details and overlook overall design issues. However, when someone else reviews it, because they weren't involved in the initial implementation process, they are more likely to discover hidden problems.

AI exhibits a similar pattern: once it generates a solution, during subsequent review, it easily continues to refine it along its established lines of thought, rather than re-examining the initial design's rationale. The result is often merely an increasingly sophisticated initial version, rather than an evolutionary process that continuously approaches a more rational structure.

On the other hand, there's a more practical issue. Since I've been using the ChatGPT Free plan, I quickly run out of advanced model credits when completing tasks that require reading a lot of content and engaging in back-and-forth discussions. Although I can still complete the task after switching to the basic model, the efficiency and stability of the entire discussion process are affected to some extent by the change in reasoning ability.

These reasons combined made me start to think: In order to solve this blog content organization problem, could we redesign the way humans and AI collaborate, instead of still having one model do all the work?

Ultimately, I decided to break the whole process down into three different roles.

I │ ├── Determine the goal ├── Final decision ├── Freeze the design boundaries └── Provide human judgment (e.g., "Something doesn't feel right here") ↓ ChatGPT (Architecture Design + Review) │ ├── Derive the overall structure ├── Maintain overall consistency ├── Control the direction of evolution └── Review each modification ↓ Other models (Qwen was chosen this time) (Exploration and Implementation) │ ├── Read the local knowledge base ├── Perform specific analysis tasks ├── Generate candidate structures ├── Iterate continuously based on feedback └── Output the organized results

The biggest change here is not replacing ChatGPT with Qwen, but rather assigning different responsibilities to the two models.

ChatGPT is no longer responsible for analyzing or generating large amounts of specific content, but instead focuses more on the overall architecture, design constraints, and review of each iteration; Qwen is responsible for analyzing, organizing, and modifying specific content according to the established rules.

Meanwhile, because Qwen is deployed locally in a chatbox and has direct access to a pre-built knowledge base containing all blog posts, it can quickly retrieve relevant content each time it analyzes a document, without repeatedly resending large amounts of articles to the model. This reduces token consumption and makes the entire iteration process smoother.

For detailed instructions on building a local knowledge base using Chatbox, please refer to the article:Practical application of Ollam's self-built embedded model + Chatbox knowledge basePlease select the embedding model according to your actual needs. For example, I am currently using Qwen3-embedding:8b, which is built on Ollam on my local m4pro macmini.

However, instead of directly importing all blog posts from the knowledge base, I now recommend using a script first (script details can be found in the article:Building a Lightweight Knowledge Index for Your Blog (Part 2): Implementation of JSON Structure and Script GenerationExport all blog posts from WordPress as a JSON file via API, then clean the JSON file and output it as a md file, which is then imported into the Chatbox local knowledge base. This allows the file to contain information such as ID, title, and URL, making it easier to locate specific posts when discussing them with Qwen.

The file I ultimately imported into the Chatbox knowledge base is knowledge-corpus.md, and the articles are stored in the following format:

More importantly, this division of labor brought about an unexpected effect. Because two different models were responsible for design and implementation, ChatGPT no longer needed to review the content it had just generated. Instead, it was like reviewing someone else's submitted proposal, making it easier to identify problems from the perspective of overall structure and consistency.

The whole process began to resemble teamwork in software development: people were responsible for defining goals and making final decisions; ChatGPT was more like an architect and reviewer, responsible for proposing directions, establishing constraints, and reviewing the evolution process; Qwen was more like a research assistant and implementer, responsible for analyzing content based on existing rules, generating intermediate results, and continuously adjusting based on feedback.

This collaborative approach allows the entire exploration process to continue: because problem decomposition, structural design, detailed analysis, and results review are distributed across different stages, I can re-examine the existing structure after each iteration, identify new problems, and then add new constraints. The whole process is not about constantly refining the first version of the solution, but rather about gradually approaching a more stable structure through repeated verification and correction.

As the discussion deepened, our focus shifted. Initially, we discussed, "How should we organize these articles?" Later, this question evolved into a more fundamental one:In the article, what exactly is the smallest unit that is worth maintaining in the long term?

3. Many concepts have been extracted, but which ones are truly worth retaining?

The last chapter left a question unanswered:In the article, what exactly is the smallest unit that is worth maintaining in the long term?

Initially, my approach was straightforward: have Qwen analyze the entire blog and extract long-term, stable, and independently existing concepts from the articles. However, the first result quickly revealed new problems. For example:

Docker containerization of Cloudflare Redis Embedding vector database

These concepts all seem to meet the initial requirements: they have clear names, can be explained independently, and won't disappear with changes to a particular article. However, when actually trying to use these concepts for long-term maintenance, the first problem arose:The same concept may be expressed in different ways. For example:

Docker containerization and container deployment

Although these names differ, they actually describe the same core concept. Similarly, there are:

Text Embedding, Embedding Technology, Embedding Model

as well as:

SSL Certificates SSL Certificate Management

Without standardization, the same concept will be repeatedly recorded as multiple nodes due to different ways of expression. Therefore, the first problem to be solved is:The same knowledge concept should retain only one stable name.

To help Qwen understand what kind of content should be a Concept, I designed a set of analytical principles:

⸻ Analysis Principles: Do not focus on: - Article titles - Categories - Publication time - Writing order - Series of articles. These are not long-term stable information. Focus on: - Long-term existing knowledge objects - Relationships between knowledge points - Knowledge units that can be learned independently - Concepts that can serve as nodes on a knowledge map. ⸻ Concept Requirements: Each concept should meet the following requirements: 1. Can be named independently 2. Can be learned independently 3. Can have its own description 4. Can have multiple related articles 5. The name should still be largely valid five years later. For example: Embedding, Transformer, RAG, Vector Database, Cloudflare, Tunnel. Do not extract: Chapter titles, Practice processes, Operation steps, Experience summaries. These belong to the narrative structure of the article or specific experiences, not knowledge objects that need long-term maintenance. ⸻ Concept Consistency: Please actively unify concepts. For example: Embedding: Do not appear simultaneously: Text, Embedding, Embedding technology, Embedding model. Retain: Embedding. Another example: Systems thinking: Do not appear simultaneously: Systems perspective, Systems thought, Systems analysis. If expressing the same core concept, please unify the name. Please also list: Concept: Embedding. Previous methods include: Text Embedding, Embedding techniques, and Embedding models. A unified approach is recommended: Embedding ⸻

With this goal in mind, Qwen began analyzing all the articles on the blog and attempted to create the first version of the Concept Dictionary. Here are some of the results:

After standardizing the names, it seemed that part of the problem had been solved. However, upon further examination of the list, a deeper issue emerged:Even though all the names have been standardized, these concepts still don't look harmonious when put together.

The reason is that while we solved the problem of "what it should be called," we didn't solve the problem of "what it actually is." For example:

Cloudflare Tunnel Zero Trust Home Server

These contents may all appear in related articles, but they actually represent completely different things:

• Cloudflare is a service platform
• Cloudflare Tunnel is a specific capability
• Intranet penetration describes the problem that needs to be solved.
• Zero Trust is a security architecture concept.
• Home servers are one application scenario.

If you save them all as Concepts at the same level, what you get is not a stable knowledge structure, but a reorganized list of keywords.

This process gradually made me realize that knowledge is not a flat list. Content that is also called a "concept" may actually come from completely different levels: some are discovered patterns, some are artificially designed methods, some are specific implementation tools, and some are ways of thinking that help people understand problems.

If these properties are not distinguished and simply unified in name and put together, what is ultimately obtained is still just a larger set of keywords, rather than a truly maintainable knowledge structure.

Therefore, in the next phase, instead of having Qwen add new concepts, I tried to further assess the properties of existing concepts themselves:

Through concept typing, we attempt to answer another question:What exactly are these concepts? For example:

Redis → Product; Reverse Proxy → Technology; Overtones → Theory; Engineering of Constraints → Thinking.

The biggest change at this stage is the shift from "identifying names" to "understanding the object itself." We no longer focus on whether a word frequently appears in an article, but rather on its role within the knowledge system.

For example, while "token" is an important concept in AI, it's more like a basic unit in the operation of a language model; while "knowledge map" is an important design element of a blog, it describes a way of organizing knowledge, not the knowledge itself; and while concepts like "multi-dialing" and "port mapping" have practical value, they are highly dependent on specific scenarios. These are not examples of erroneous extractions, but rather unsuitable for long-term maintenance.

Through continuous adjustments and verification, I gradually discovered that what we really need to find is not keywords in articles, nor smarter tags, but a content object that can exist in the long term—it should be able to exist independently; it should be able to appear repeatedly across different articles; it should not change its identity due to changes in expression or application scenarios; and it should be able to become the basis for establishing relationships with other knowledge.

For example, there are two articles: one introduces how to configure Cloudflare Tunnel; the other discusses how a home server can expose services through Cloudflare Tunnel. The two articles focus on different issues, but they ultimately point to the same object: Cloudflare Tunnel.

What truly requires long-term maintenance here is not the "configuration method" or "deployment scheme" in a certain article, but the stable object "Cloudflare Tunnel".

Later, while further researching this issue, I discovered (actually, GPT suddenly brought it up) that there is a field specifically dedicated to studying this type of problem: Knowledge Engineering. Knowledge Engineering focuses on how to transform fragmented information into structured knowledge, and how to ensure that the objects, attributes, and relationships within this knowledge can be maintained over the long term.

In this field, content objects that can exist stably and maintain a consistent identity across different modes of expression are called Entities. Entities focus not on "how many times this word appears in an article," but rather on "whether expressions in different articles refer to the same stable object."“

After all that, it turns out that what I've been researching was something someone else summarized decades ago.

4. The concepts are already stable, so why can't the knowledge system be built?

At the end of Chapter Three, we finally found an object that was truly worth maintaining in the long term. At the time, I thought that as long as we continued to improve this Concept Dictionary, the entire knowledge system would be complete.

In fact, for a long time afterward, that's exactly what we did: continuously add missing concepts, correct names, adjust categories, and redefine what content should be kept and what content should be deleted. As the iterations continued, this Concept Dictionary became more and more complete and stable.

But at this moment, a new problem arose.It can answer the question "What knowledge objects are in the blog?", but it still cannot answer the question "What is this blog about?".

When Qwen was asked to conduct a self-check, it described it as follows:

Looking back now, this passage almost perfectly describes the predicament we encountered at the time. For example, in Concept Dictionary, we already had some stable objects like these:

Cloudflare Tunnel Home Data Center Constraint Engineering

These are all real, long-term, and stable content objects. However, simply possessing these objects cannot answer the following questions:

• Why did the home data center ultimately choose Cloudflare Tunnel?
• Why wasn't a public IP address or port forwarding used?
• How does the concept of "constraint engineering" influence the design of the entire home data center architecture?

Concept Dictionary cannot answer any of these questions. This is because it records...WhatHowever, there was no record.Why are they connected?.

To move forward, we initially tried several very intuitive methods.

The first method is to establish connections based on common occurrences within articles. For example, Cloudflare Tunnel and reverse proxies frequently appear in the same article, so a line is drawn between them. However, this method proved largely meaningless. Co-occurrence does not necessarily indicate a stable knowledge link.

The second approach is to utilize semantic similarity. Since embeddings can calculate the distance between two objects, does that mean the closer the distance, the more likely they are to be connected? Practice shows this is also incorrect. Semantic similarity only indicates that they discuss similar topics, but it doesn't explain what that similarity actually means. Docker and Podman can be very similar, as can Cloudflare Tunnel and Zero Trust, but this similarity doesn't reveal the true relationship between them.

The third method is simpler: just draw a line and label it "Related". However, it was almost immediately abandoned because "related" can represent almost any relationship. And being able to represent any relationship means that nothing is being expressed.

At this point, we realized that this Concept Dictionary was no longer able to be improved. What truly hindered the continued development of the entire knowledge system was no longer the objects themselves, but the lack of a long-term, maintainable connection between these objects.

Using the same example again, if we only write:

Cloudflare Tunnel ↓ Home Data Center

Therefore, this line is almost worthless. It only indicates that two objects appeared at the same time, but it cannot explain the relationship between them.

The real question is: Why did I choose Cloudflare Tunnel?

Only after reviewing the entire series of articles on home data centers will one realize that this is not a simple software selection, but a solution that has gradually evolved under various practical constraints such as public IP address, network environment, and maintenance costs.

In this process, what's truly worth maintaining isn't the fact that "Cloudflare Tunnel is related to home data centers," but rather the underlying structure:

Network limitations ↓ Problems arising ↓ Architecture selection ↓ Technical implementation ↓ Final application

In the context of specific knowledge objects, it is not a simple connection, but rather multiple relationships with clearly defined meanings:

The lack of a public IP address leads to the need for an internal network service exposure solution, Cloudflare Tunnel, to achieve reverse proxy capabilities in home data centers.

Only by clearly defining what these connections represent can a knowledge system express "why this is so," rather than merely recording "that they once appeared together."

It was only then that I realized that the Concept Dictionary I had been maintaining was actually just an increasingly refined dictionary. What a knowledge system truly needs to maintain is not the objects themselves, but the stable, clear connections between them that accurately express meaning.

In knowledge engineering, these connections that have clear semantics and can accurately express the relationships between objects are called... Relationship.

Its focus has shifted from "what objects exist" to "what relationships exist between these objects." My strongest feeling at the time can be summed up in one sentence:If I had known this concept earlier, I could have avoided a lot of detours.

5. Why do I start to doubt my knowledge structure as it becomes more and more complete?

After several rounds of adjustments, the entire knowledge system has become increasingly complete. Concepts have been unified and categorized, and various relationships have been established between objects. The initial idea was that as long as these relationships were continued to be added, the knowledge system of this blog should gradually be perfected.

However, during a subsequent re-examination of the current knowledge structure, Qwen raised a question that I had never expected:

When I first saw this sentence, I was taken aback. Everything had been going smoothly in the previous rounds, so why did this problem suddenly appear? As I continued reading, I realized it pointed to a very subtle issue in the entire project—"industry best practices" disguised as underlying logic.

Qwen then raised a question that I could hardly refute:If all the articles in the blog are deleted, and only these objects and their relationships are kept, will this diagram still hold true? The answer is:It still holds true. This means that this network likely describes the entire technology world, rather than just this blog.

At this point, I realized that the real problem with the relationships established earlier wasn't whether they were correct. In fact, many of the relationships Qwen provided were technically sound. The real problem lay in:Who exactly established these relationships?

If these relationships originate from the blog posts themselves, then they describe the insights developed through the blog's long-term practice. However, if these relationships are simply automatically filled in by Qwen based on her own knowledge, then they merely describe the technical common sense generally accepted across the industry.

The biggest difference between the two is that the former belongs to this blog, while the latter belongs to the entire tech world. For example, Qwen can easily deduce a standard technical roadmap based on his own knowledge. However, when you actually return to the context of the article, you'll find that what I'm really concerned about is not the standard solutions in the industry, but why, under specific constraints, a completely different roadmap is ultimately chosen.

In other words, what the article truly preserves is not just the final conclusion, but also...The entire thought process that led to this conclusion.

It was only then that I finally understood that what we truly needed to maintain was no longer just whether there were relationships between objects. More importantly:Did these relationships actually originate from the article itself?

So, starting from this round, the questions we discussed changed. Previously, we kept asking: Is there a relationship between the two objects?
Later, they began to keep asking questions:On what basis should this relationship be established? If this question cannot be answered, then even if the relationship is technically correct, it cannot be considered the blog's own knowledge.

Only when we can return to the specific articles and find out why I made that connection in the first place, will this knowledge network truly belong to this blog, and will it not slowly evolve into a technical encyclopedia applicable to everyone as Qwen continues to reason.

This issue has long been considered in knowledge engineering. In addition to the relationships between objects, many knowledge engineering practices further focus on the sources and evidence of knowledge.

The purpose of evidence is not to prove whether the relationship is theoretically correct, but to answer another more important question:Why can this relationship be established?

Only when every connection can be traced back to the actual content of the article can this knowledge network truly belong to the author, and will it not gradually become a technical encyclopedia applicable to everyone through repeated reasoning by AI.

6. Define the basic structure of the knowledge system—knowledge-schema.md

Once the concepts of Entity, Relation, and Evidence have become clear, the next question is: how to translate these understandings into a practical structure that can be maintained in the long term.

Because the knowledge system isn't a one-time compilation but needs to grow continuously with the blog, I first needed to define its basic structure. To this end, I designed... knowledge-schema.mdThis describes how the entire knowledge system should be organized. It doesn't simply define a few fields, but rather answers a more fundamental question:What components should a long-term, maintained knowledge system consist of?

The preceding chapters have gradually clarified three core questions: What knowledge objects in the blog are worth preserving long-term? What stable relationships exist between these objects? How can these relationships be traced back to the specific facts in the articles? These three questions correspond to Entity, Relation, and Evidence, respectively.

However, when these concepts are actually implemented in a long-term maintained system, simply knowing "what is there" and "how they are related" is not enough. Further clarification is needed: How should this information be organized? How should responsibilities be divided among different types of information? How should the system be expanded upon with the addition of new content, rather than constantly being overturned and rebuilt?

therefore,knowledge-schema.md The purpose of this is not to define a few data fields, but to establish a stable framework for the entire knowledge system.

Ultimately, I defined a 5-layer structure for knowledge-schema.md:

This structure shows thatknowledge-schema.md In essence, this defines the boundaries of responsibility between different types of information: Entity is responsible for storing stable knowledge objects, Relation is responsible for describing the meaningful connections between objects, and Evidence is responsible for recording the factual basis for establishing these objects and relationships. These three are independent yet complementary, together forming the core structure of the knowledge system.

After several rounds of practical verification, the responsibilities of these three core layers have become largely stable, and therefore they are marked as "frozen" in the schema. This "frozen" does not mean that these contents will never change, but rather that their basic responsibilities have been determined, and subsequent expansions should prioritize adhering to existing boundaries rather than repeatedly modifying the basic definitions.

This separation is crucial because if all the information is mixed together, as the content increases, the knowledge system can easily degenerate back into another form of tag set: what words are in the article; which words often appear together; what content looks similar—which is precisely the problem that the previous chapters have been trying to solve.

In addition to the first three basic layersknowledge-schema.md Two higher-level directions are also reserved in the middle.

The first is the Knowledge Edge Layer. It focuses not on the knowledge objects themselves, but on how different knowledge forms reading paths. For example, a basic conceptual article may become prerequisite knowledge for another practical article, and an early solution may evolve into part of a subsequent architecture.

The second is Topic Space. It focuses on how to observe the thematic structure of the entire knowledge system from a higher level as knowledge objects accumulate.

However, both of these approaches are built upon the stabilization of the first three layers. Only by clearly identifying which objects exist, how they are connected, and whether these relationships are based on evidence, can subsequent knowledge navigation and topic discovery have a reliable foundation.

Looking back,knowledge-schema.md It wasn't a technical specification designed from the beginning, but rather a structural summary that gradually formed after several rounds of practice. What was ultimately fixed wasn't a few fields, but a set of basic principles for maintaining a knowledge system in the long term: what should be saved; how different information should be divided; how new knowledge should enter the system; and how the existing structure should continue to evolve.

If the preceding chapters were an exploration:How to discover a person's knowledge system from a large number of articles?So this chapter addresses the following:How can we ensure that this knowledge system can exist for a long time and continue to grow over time?

Note: Forknowledge-schema.md完整版内容感兴趣的朋友可以通过以下链接将文件另存到本地查看(需要支持md格式的阅读软件)：knowledge-schema.md.

7. Postscript: An Unexpected Gain from AI Collaboration

Looking back at the whole process, what surprised me the most was not the final creation of knowledge-schema.md, but that what started as a problem to improve the blog content discovery experience eventually evolved into an exploration of personal knowledge structures.

What's even more interesting is that this process doesn't start with a predetermined method, but rather gradually approaches existing ideas in a mature field by continuously solving practical problems. Concepts like Entity, Relation, and Evidence aren't theoretical frameworks introduced from the beginning, but rather answers that naturally emerge when faced with specific questions such as "what is worth preserving," "how knowledge is connected," and "how the source of these connections is proven."

This is also the most valuable aspect of this exploration: it made me realize that many methods in a professional field don't necessarily have to start with theoretical learning. Sometimes, a real problem is complex enough that, as one keeps trying to solve it, one may gradually move towards methodologies that have already been summarized in a certain field.

This experience has once again validated some of my previous thoughts on "using AI for cognitive collaboration": the value of AI is not merely to replace a specific step, but to extend human capabilities in complex cognitive tasks (see related content:Creative Reconstruction in the AI Era: From Information Processing to Cognitive Collaboration).

However, this extension does not simply rely on stronger model capabilities, but rather on redesigning the way humans and AI collaborate, allowing different roles to undertake different types of cognitive tasks. AI is not a system that can autonomously construct knowledge, but rather a cognitive ability amplifier that needs to be guided, constrained, and organized.

The value of this division of labor lies not merely in adding another execution model, but in allowing different cognitive aspects of complex tasks to be handled separately. In the past, an AI often needed to simultaneously determine direction and execute details, easily consuming its reasoning abilities through extensive content organization and repeated revisions. In this collaborative model, Qwen can focus on specific content processing, ChatGPT can maintain a higher level of structural review, and humans are responsible for providing real-world experience and final judgment, thus creating a clearer division of labor between the previously intertwined stages of problem analysis, solution design, execution, and outcome evaluation.

This exploration, ostensibly about building a better content index for the blog, was actually also an attempt to explore how AI can assist humans in performing complex cognitive tasks. It showed me that the future value of AI may not just be replacing a specific step, but rather helping individuals complete tasks such as problem analysis, structural design, and knowledge organization—tasks that previously typically required specialized teams—through collaboration between different roles.

Knowledge management is precisely the scenario in which this collaborative approach is best suited. The knowledge that an individual accumulates over a long period of time is not simply a pile of information, but a process of continuous understanding, judgment, and correction. AI can help us organize information, discover connections, and build structures, but what is truly worth preserving in the long term is still the way of understanding, judgment process, and cognitive path that a person develops in practice.