ChatGPT × WordPress Development: The Complete Guide to High-Performance “Context Design”– Ashita wa Harerukana –

This guide is designed to help you overcome a common challenge in AI-assisted development: when conversations with ChatGPT start to drift, lose alignment, or produce inconsistent results.

This page is part of the technical documentation and development knowledge base for “Ashita wa Harerukana,” a regional event information platform. Our site is uniquely built by combining WordPress and ChatGPT, and this guide organizes the practical development insights and AI-utilization techniques gained through that process.

Although the platform primarily serves local communities, it operates with numerous backend modules that support broader “regional digital transformation.” For that reason, in-depth technical documents like this are published as a dedicated series.

“Chat Structure Design” refers to the technique of structuring conversations so that AI can respond accurately and consistently without losing track of context.

This guide focuses not on general AI usage, but on the practical method of structuring and separating chats so development stays productive and stable.

The practices introduced here are based on real, field-tested knowledge—verified through more than 80 days of continuous development work carried out collaboratively by R-san and ChatGPT.

This guide is not about ChatGPT’s UI features such as “Start a new chat.” Instead, it explains:

“Chat Structure Design” — a practical method for managing multi-module, high-frequency WordPress development without allowing AI confusion or context collapse.

This knowledge has been built through real development and will continue to evolve as new features and updates are introduced—an ever-growing body of practical expertise.

Rather than presenting general theories found on many websites, this guide explains only reproducible, proven techniques that supported large-scale development in practice.

1. What “Chat Branching” Means in This Guide (Distinct from ChatGPT’s UI Feature)

ChatGPT includes a UI option labeled “Start a new chat,” but the “Chat Branching” described in this guide refers to an entirely different concept.

Here, it describes:

A structured way of dividing conversations by purpose so AI can respond accurately without losing context.

The “Ashita wa Harerukana” project, co-developed by R-san and ChatGPT, operates in an environment that is particularly challenging for AI:

• Multiple interconnected modules
• Frequent updates
• Heavy use of external APIs
• Long-running, multi-task workflows
• Development, operations, and content writing happening simultaneously

Despite this complexity, development did not collapse. The key was the gradual refinement of a structured conversation rule — what we now call “Chat Branching.”

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2. Why Separating Chats Maximizes AI Performance

The more past conversation history ChatGPT must consider, the more likely it is to experience:

• Vague reasoning
• Misinterpretation of context
• Irrelevant topic pickup
• Slower responses
• Difficulty updating assumptions

This is not a weakness of the model, but an inherent limitation of how context windows function.

• Frequent specification changes
• Numerous modules
• Large numbers of files
• Deep interdependencies

In such complex development environments, continuing in a single chat will inevitably lead to breakdown.

R-san experienced a “context collapse period” in which AI misunderstandings compounded. The breakthrough came from introducing structured Chat Branching.

3. Core Principles of Chat Branching: Purpose, Domain, and Phase

■ Principle 1: Separate by Purpose

• New feature development
• Debugging
• Specification clarification
• Writing articles
  Chats may remain unified as long as the purpose does not change.

■ Principle 2: Separate by Domain

• Development (Development Mode)
• GPT Design (Custom GPT Mode)
• Content Writing (Writing Mode)

These domains involve different kinds of reasoning. Mixing them leads to misinterpretation.

■ Principle 3: Separate by Phase

• Design → Implementation → Testing
  Whenever the phase shifts, so do the assumptions—meaning a new chat is required.

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4. When Should You Start a New Chat? (Practical Criteria)

Based on experience, starting a new chat is overwhelmingly beneficial in the following situations:

✔ When a module is completed → Start a new chat

✔ When the subject of the task changes → Start a new chat

(Example: Facility API → Event Form → SEO Module)

✔ When reasoning becomes vague or slow → Start a new chat

(A sign that the context window is overloaded.)

✔ When AI continues based on a misunderstanding → Start a new chat

(Indicates accumulated context drift.)

On the other hand, minor adjustments (wording, small diffs) can remain within the same chat.

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5. Chat Granularity: Not Too Broad, Not Too Narrow

“Chat granularity” means defining how much content or how many topics belong in a single chat. This boundary-setting is the most crucial factor influencing AI reasoning accuracy.

Given the principles of separating by purpose, domain, and phase, the key is to maintain appropriate granularity within each chat.

ChatGPT’s performance is strongly influenced by the amount and range of information contained in each chat.

■ If granularity is too broad

• Too much information increases misinterpretation
• Chronology of specifications becomes unclear
• Similar but different modules get mixed
• The AI may “solve the wrong problem”

■ If granularity is too narrow

• Context must be re-established every time
• Chat organization becomes the task itself
• One piece of work becomes scattered across multiple chats

■ Why granularity matters most

ChatGPT is powerful, but it relies heavily on a clear boundary: “What belongs in this chat, and what does not.”

When granularity is appropriate:

• Context is preserved correctly
• Reasoning becomes precise
• Unnecessary revisions decrease
• Development speed improves

When granularity is inappropriate:

• Misreadings increase
• Context becomes tangled
• Old specifications are misinterpreted
• Conversations drift off-topic
• Reasoning becomes unstable

These problems will inevitably occur.

■ And importantly: humans determine the granularity

ChatGPT cannot automatically decide, “This topic should now be separated into a new chat.”

Throughout development, R-san naturally adjusted granularity whenever:

• The topic shifted significantly
• The module being worked on changed
• The project entered a new phase
• Misreadings increased

Since AI performance is built upon appropriate granularity, this is one of the most important human responsibilities in AI-assisted development.

🔑 Key Insight: Granularity Defines the Boundary Between Human and AI

• If granularity is too broad, the AI becomes confused.
• If it is too narrow, humans become exhausted.
• At the optimal level, AI performance is maximized.
• Chat branching and granularity work as a pair.
• Adjusting granularity is a critical human judgment in collaborative AI development.

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6. Practical Examples: Chat Branching That Worked in the Project

Here are examples of chat branching methods that were actually used in the “Ashita wa Harerukana” project—and that produced strong results. These are not abstract theories, but concrete patterns proven effective in a complex WordPress × AI development environment.

● Branching by Module

(Examples that successfully worked in practice)

• SEO Module
• Facility Auto-Generation (Google Places)
• OCR Auto-Fill (Vision)
• Regional Taxonomy
• Event Form
• Mail Module
• Wiki Authentication
• RSS/JSON Feeds

● Branching by Phase

• Design → Implementation → Testing → Release → Operations

Separating chats by development phase was highly effective in preventing context entanglement.

● Article Writing in Separate Chats

• Writing chats must never mix with development chats.

Mixing writing tasks with development tasks often confuses the AI. Simply separating them dramatically improved response accuracy.

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7. Common Pitfalls: What Happens When You Keep Everything in One Chat

When a project is carried out within a single chat over a long period, the following problems inevitably occur:

• AI continues answering based on outdated assumptions
• Code consistency breaks down
• Implementation and discussion drift apart
• Multiple topics get mixed, causing AI to pull in the wrong context
• Revised assumptions fail to apply
• Debugging becomes slow and repetitive

These issues arise not from AI limitations, but from inadequate conversation structure management.

Since ChatGPT references the entire conversation history when reasoning, overloading a single chat causes context compression, misinterpretation, and tangled logic.

🔧 So what’s the solution? (It’s surprisingly simple.)

As soon as problems begin to appear, simply start a new chat and resume the task there. Doing this dramatically improves the situation.

• A new chat → Only the necessary information is provided → AI reasons with a clean context → Accuracy improves and processing speed increases

This means that effective Chat Branching is not a complex technique—it is a simple, repeatable operational rule:

“When needed, switch to a new chat.”

■ Topics: How R-san Performs “Context Reset” in Practice

During development, whenever responses felt slightly misaligned or based on incorrect assumptions, R-san would say:

“Discard the content of this chat.”
Then immediately continue debugging in a fresh chat.

This simple action has a profound impact on ChatGPT’s performance:

• Context compression residue resets to zero
• Reasoning restarts based solely on current assumptions
• Incorrect or outdated context is fully discarded
• Debugging direction becomes immediately clear

This “chat switching” is the fastest and most effective tuning method for maximizing AI performance.

Many users mistake these symptoms for AI degradation, but R-san accurately identifies them as structural fatigue and responds appropriately.

This is an advanced operational skill in AI-assisted development.

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8. How to Create Effective Chat Branching (Practical Templates)

■ When starting a new feature

State the purpose ↓ Share 1–2 key points of the current situation ↓ Open a dedicated chat for the implementation phase 

■ During debugging

Report only the diff ↓ Open a chat focused solely on the problematic area 

■ For operations and article creation

Always use a separate chat 

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9. Summary: Chat Branching Is the Foundation of High-Performance AI Development

Unlike the UI option “Start a new chat,” the Chat Branching described here is a deliberate technique for designing the collaborative structure between AI and developer.

The reason the R-san approach remained stable even in large-scale development is that Chat Structure Design was rigorously applied from the earliest development stages.

Chat Branching is the first layer of design in AI co-development— it shapes performance, speed, accuracy, and long-term stability.

By applying these principles, your WordPress × ChatGPT × collaborative coding workflow can achieve maximum speed and accuracy.

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English Version: ChatGPT × WordPress Development: The Complete Guide to High-Performance “Context Design”

📚 WordPress × ChatGPT Practical Knowledge Series

Series:

• ✅ Chapter 1: Chat Branching Guidelines (this page)
• 👉 Chapter 2: Prompt Design Techniques
• 🔜 Chapter 3: Designing the AI–Human Verification Loop (Coming soon)