― Basics of Prompt Design and the “Communication” Mindset that Determines Results ―
Have you ever used Generative AI and felt that “the answer wasn’t what I expected,” “the content was shallow,” or “the results vary every time”? In many cases, the cause is not the AI’s performance, but the design of the instructions (Prompt Design).
This article explains the “instructional mindset” that is effective for any Generative AI, regardless of specific tools or features. This is foundational knowledge for moving from “using AI by guesswork” to “consistently drawing out desired results.”
Why the Way You Give Instructions Determines Results
Generative AI appears to understand human language, but it actually processes the meaning of words probabilistically as context. In other words, the AI infers from your instructions “for what purpose, under what conditions, and in what format” it should provide an answer.
If the instructions are vague, the inference becomes vague, leading to:
- Responses leaning toward generalities.
- Off-target premises.
- Unusable lengths or formats.
Conversely, if the instructions are organized, AI becomes a powerful entity that assists human thought.
The Essence of Prompt Design: A “Request,” Not a “Question”
Input to AI is often thought of as a “question,” but those who produce results in business give instructions with the mindset of writing a “work order.”
For example:
- Incorrect: “Analyze this data.”
- Correct: “Using this sales data, please summarize the key points for my supervisor in about 300 characters, focusing on year-over-year ratios and differences.”
The latter includes the following information: Target data, Perspective for analysis, Reader, Character count, and Output format. This “resolution of the request” greatly determines the output quality.
4 Elements Common to Good Prompts
The basics of universal prompt design, independent of tools, are the following four points:
- Purpose (What is it for?): Do you want material for judgment, an explanatory text, or a brainstorm of ideas? If the purpose is vague, the AI will default to a “safe/generic response.”
- Prerequisites (Background/Constraints): Industry, standpoint, target audience, expertise level, and usage scenario. Even in human conversation, if premises differ, communication fails. AI is the same.
- Perspective (What should it look at?): Axes of comparison (differences from last year/month/average), risk/improvement perspectives, or positive/negative views. Specifying perspectives makes it easier for AI to select information.
- Output Format (How will it be used?): Bullet points vs. paragraphs, character count, and the presence of headings. Deciding the format reduces “unusable answers.”
Common Failure Patterns in Prompt Design
- Failure 1: Seeking perfection in a single instruction. AI performs best when “dialogue” is assumed. Accuracy improves if you have it produce a draft first and then adjust from there.
- Failure 2: Overusing abstract words. “Easy to understand,” “Good feeling,” or “Thoroughly.” These may work with humans, but AI can interpret them in many ways. It is effective to replace them with numerical values, conditions, or specific examples.
- Failure 3: Outsourcing the “Human Thinking” part. Instructions like “Please decide how I should judge this” are dangerous. It is more realistic and safer to use AI as an entity that organizes the materials for judgment.
The “Step-by-Step Prompt” Concept for Practical Work
Quality is more stable when you divide the process: Organize → Extract Key Points → Create Explanatory Text → Adjust for the Reader. This flow is the same as the order in which humans think. Having the AI trace this sequence stabilizes the quality of the output.
Summary
The ability to master Generative AI is not the “ability to write difficult commands.”
- Verbalizing the purpose.
- Organizing the premises.
- Deciding the usage. This capacity for “thought organization” itself holds the most value in the AI era.
Service Introduction: Implementing This Mindset into Practice
We offer a service that makes the concept of “organizing purpose, premises, perspectives, and output format” easy to use in daily operations.
By linking Excel and ChatGPT, we expand the scope of automation—which previously relied on manual labor or Macros/VBA—to include statistical processing, analysis, summarization, and visualization. We realize both efficiency and quality improvement by automatically generating data shaping, summaries, anomaly detection, and analytical comments based on natural language instructions.
This service is for you if:
- You use AI, but the results are inconsistent.
- You spend too much time creating comments or explanations for Excel tasks.
- You want to quickly create outputs that can be used for decision-making.