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ChatGPT Prompt Engineering Reaches the Next Dimension! Practical Technique Synthesis as Seen in Beginner Support AI

2025年5月18日 10:10

Behind the "friendly AI" capable of answering even vague questions politely lies a combination of multiple prompting techniques. We explain practical design examples fully compatible with GPTs!

Chapter 1: Practical Application! What is the "Synthesis" of Prompting Techniques?

When ChatGPT's responses go beyond "just sounding like an AI" and become as natural as a human support agent—
such prompt design is achieved through the "synthesis" of multiple techniques rather than a single method.

In this article, I will look at
how I combined and designed them for practical use using the following techniques I actually used in the "PC Beginner Support AI" I built.

By synthesizing individual prompt techniques through "role division," natural interaction can be achieved.

Chapter 2: What Techniques Were Used and What Are Their Purposes? Visualization in a List!

The table below is a correspondence map of the prompt techniques used in this build, their respective purposes,
and "how they were used in this build."

Correspondence map of "how they were used in this build"

✅Structured Prompting: Control output to a fixed format: Standardize id/category/question/answer in JSON format
✅Few-shot Schema Transfer: Expand many patterns from a few examples: Utilized for automatic generation of 200 FAQs from 40
✅Intent Disambiguation Prompting: Infer the intent of vague expressions: "The screen won't do anything..." → Reconstructed as "Is the power not turning on?"
✅Step-by-Step Reasoning: Guide politely in step format: Break down and present operation procedures clearly
✅Prompt Flow Design: Design the flow of conversation: Consistent flow of trigger → category → question → answer → supplement
✅Knowledge-grounded Prompting: Utilize knowledge base: Build all answers based on JSON-formatted FAQs

Each prompt technique plays a different role, such as "structuring," "inference," and "flow design."

Chapter 3: Operational Structure of "Friendly AI" That Is Easy for Beginners to Use

The actual user experience follows the flow below.

Launch with "Start"
Select a category
Select a question or make a vague statement
Friendly explanation + steps + supplementary knowledge
Markdown format output

The point is that everything coexists with natural conversational tone and **strict structuring (JSON)**.

You can build a consistent "friendliness design" from start to supplement.

Chapter 4: Output Format for Full Integration with GPTs

You can load this into ChatGPT's custom GPTs (GPTs) using the following JSON format.

{
  "id": "qa042",
  "category": "インターネットのつながりが悪い",
  "question": "Wi-Fiが途切れるんですけど…",
  "keywords": ["Wi-Fi", "接続", "不安定"],
  "answer": "Wi-Fiが不安定なんですね。もしかするとルーターとの距離や電子レンジなどの干渉が原因かもしれません。",
  "steps": [
    "ルーターの近くに移動してみてください",
    "他の機器の電波を止めて試してください",
    "それでも不安定ならルーターの再起動を行ってください"
  ]
}

By making the output format compatible with GPTs, large-scale deployment and automation become possible.

Chapter 5: The 'Strength' and 'Scope of Application' of this Configuration

There are three reasons why this type of configuration is excellent.

It achieves both natural conversation and structure.
It does not break down even when knowledge is increased.
It has high compatibility with GPTs and is reusable.

Furthermore, it can be applied in the following scenarios as well.

Knowledge generation for NOTE articles and blog posts
FAQ chatbots for local governments and schools
Automation of help desks and reception chats

A configuration that can be repurposed anywhere, from education and public services to corporate support.

Chapter 6: Advanced Ideas to Try Next

Integration with RAG (Retrieval-Augmented Generation)
Dynamic switching of JSON DSL by use case
Expansion through slide and book templating

Further development becomes possible based on this configuration.

Chapter 7: Summary—Prompting is All About 'Combination'!

AI performance is determined by prompts. However, the true 'strength' lies in the 'power to combine techniques'.

Learning individual techniques is not enough.
How to layer them, how to incorporate them into a flow.
That is the dividing line in GPT utilization.

Why not try building an AI that can properly pick up on even vague questions?

🔍 Summary in one sentence

Prompt techniques show their true potential through 'combination'! A strategic design example for creating AI that is friendly even to beginners.