GenerativeAI-Prompt-Sample-Japanese: A Japanese Prompt Library for ChatGPT and Copilot

What GenerativeAI-Prompt-Sample-Japanese actually contains

The repository is a collection of Japanese-language prompt samples for generative AI tools, described in its README as サンプル, samples. It is not a runtime, a wrapper or a framework. There is no package to import and no API surface. The primary language listed for the repository is HTML, and the top-level layout is a set of directories named after job functions and task categories: Agent, Code Interpreter, DALL-E, Security, Software Engineer, Workshop, Workshop_English, sample, データ分析, ビジネス戦略や分析, 人事, 会議, 便利なテクニック, 営業, 教育, 数理最適化問題, 文書・画像作成, 日々のタスク, 管理職, 行政, 音声入力.

The intended reader is someone who already has access to a generative AI chat interface and is dissatisfied with the output. The README frames this directly: if your prompt's result is far from what you expected, try the examples. Two tools are named as the assumed targets, Microsoft 365 Copilot at m365.cloud.microsoft/chat and Azure OpenAI Service, with a link to the Japanese Azure OpenAI overview page. The samples are written against those environments, which matters because Copilot in particular is grounded in an organisation's own tenant data.

The disclaimer at the top is worth reading before anything else. It states that use is entirely at the user's own risk, that sensitive or confidential information should not be entered, that output should not be taken at face value, and that intellectual property and legal questions should be checked with a legal specialist inside your organisation. That last point is not boilerplate for a repository that is essentially a corpus of pasted-in meeting transcripts.

The before-and-after pattern in the 会議 examples

The README's most substantial section is the set of 会議 (meeting) examples, and it follows one consistent method: show a weak prompt, then show a rewritten prompt against the same input, then explain the difference implicitly through the rewrite itself.

The first pair starts from a single-sentence instruction, 会議の議事録を作ってください. The improved version names the input explicitly, states which fields the output must contain (participant names, agenda, statements, decisions, next actions), and then supplies the full transcript under a 会議内容 heading. The prompt is doing two jobs at once: specifying the output schema and delimiting the source material. That is the transferable idea, and it is more useful than any single sample.

The second pair targets a different failure. The weak prompt is この会議の内容をなんかいい感じにまとめて, roughly "summarise this meeting nicely somehow." The rewrite asks for a weekly report aimed at a project manager, with an explicit order: 概要, 課題, 対応策, 次のステップ. Notice that the audience changed. The first prompt asks for a summary; the second asks for a document a specific person will act on. The repository's position is that naming the reader is part of the prompt, not an afterthought.

The third example covers multi-turn use. After feeding a sprint review transcript, the README continues with short follow-ups: 技術的課題の優先順位を整理してください, then 各課題に対して具体的な技術的アプローチ案を3つずつ出してください, then さらに、PM視点でのリスクとその緩和策も併記してください. The section's own advice is to keep going if you have not yet found the points you needed. That is a reasonable description of how these models behave in practice, and it is the one place where the repository recommends a workflow rather than a single prompt.

Getting started with GenerativeAI-Prompt-Sample-Japanese: there is nothing to install

The repository has no install step, no dependencies and no build. The README points users at Microsoft 365 Copilot at https://m365.cloud.microsoft/chat and Azure OpenAI Service at https://learn.microsoft.com/ja-jp/azure/cognitive-services/openai/overview, the two tools the samples are written for. The only setup work is getting a copy of the files so you can read the samples offline and adapt them.

There is no configuration file, no environment variable and no port. The only file with operational weight is LICENSE, which is MIT. If you want to see the licence text before reusing a sample internally, read that file directly. The repository's own README does not restate the licence terms, so the LICENSE file is the authoritative copy.

A first real use is the meeting-minutes rewrite. The README gives a weak prompt and a better one side by side. The weak version is a single sentence.

会議の議事録を作ってください。

The better version names the fields the output must include and supplies the source material under a heading.

以下の会議内容をもとに、要点を整理した議事録を作成してください。参加者名、議題、発言内容、決定事項、次回アクションを含めてください。

Paste the second prompt into your chat tool, replace the sample transcript that follows the 会議内容 heading with your own text, and check whether the output includes the fields the prompt names. If a field is missing, the prompt, not the model, is usually what needs adjusting. The repository's own note is that some of these techniques will become unnecessary as models improve, which is an honest framing for a document that is essentially a snapshot of current prompting practice.

Where the sample collection stops being the right tool

The repository is a set of prose prompts in Markdown and HTML. It is not versioned as a prompt package, there are no evaluation results, and there is no changelog describing which prompt changed when. If your team needs prompts under review, with regression tests against a fixed input set, this repository gives you nothing to build that on. You would be copying text out of a document and into whatever system you actually run prompts from.

Language coverage is the second boundary. Despite the English-named folders, the description is explicit that these are 日本語 prompts, Japanese-language samples. Workshop_English exists, but the README's worked examples and the bulk of the directory names are Japanese. If your users write prompts in English, the transferable value here is the structure (state the input, name the output fields, name the reader), not the wording.

The third boundary is the one the README itself raises. Meeting transcripts are exactly the kind of input that tends to contain names, project codenames, internal system details and client information. The repository tells you not to enter sensitive or confidential information and to check IP and legal questions with a specialist. It provides no mechanism to help you comply: no redaction step, no placeholder convention for anonymising a transcript before pasting it in. The samples as written are full of realistic Japanese names, project names and dates, which makes them easy to read and easy to copy wholesale into a tool that logs input. That gap between the warning and the material is the most important thing to notice about this repository.

Finally, there are no releases. The repository's history is a sequence of pushes to main, with the last push on 2026-09-06. There is no tagged version to pin, so "which version of the prompt did we use" is not a question this repository can answer.

How this differs from a prompt framework or a curated prompt marketplace

The closest alternative in kind is a prompt framework such as Microsoft's own prompt engineering documentation or a general-purpose prompt library, and the difference is in what you get. A framework gives you abstractions: templates with variables, sometimes a client library, sometimes evaluation hooks. This repository gives you finished Japanese text with realistic filler already in place. That is easier to read and harder to operationalise.

A second alternative is writing your own prompts from scratch against your own documents. That approach wins on fit and on confidentiality, because nothing leaves your environment and every example is drawn from your actual work. It loses on the blank-page problem, which is precisely what this repository addresses. The 会議 examples are useful mainly as a shape to imitate: input block, explicit output fields, named audience, ordered sections.

A third comparison is tool-specific prompt guidance. Microsoft publishes guidance for Copilot and Azure OpenAI separately. Those documents explain how grounding, retrieval and system prompts work in those products. This repository does not explain any of that. It assumes you already have access and just need a better prompt. If your problem is that Copilot is not retrieving the right internal document, no sample here will fix it, because the samples are about phrasing, not about retrieval configuration.

The honest summary is that this is a reference document with a narrow but real use: it shortens the distance between a vague Japanese instruction and a structured one. It is not infrastructure.

Licence and the cost of keeping up

The repository is MIT-licensed. In practice that means you can copy prompts into internal documents, adapt them, and ship them inside a product, provided you keep the copyright and permission notice with any substantial portion you redistribute. This is not legal advice, and the repository's own disclaimer pushes IP questions to your organisation's legal specialists, which is the right place for them given that the samples were written against real-looking meeting content.

Upgrade cost is close to zero in the technical sense and non-zero in the editorial sense. There is nothing to upgrade: no dependency graph, no breaking changes, no migration. What changes is relevance. The README itself anticipates this, noting that some techniques will become unnecessary as models evolve. A prompt that compensates for a model's weak instruction-following becomes dead weight once the model follows instructions reliably, and a prompt tuned to one model's quirks may underperform on another.

Maintenance is light but real. The last push to main was on 2026-09-06, and there are no tagged releases, so the only way to know what changed is to read commits. If you adopt samples from here, the practical move is to copy the ones you use into your own repository and note the commit you took them from, because this repository will not tell you later which prompt you were looking at.

Editorial conclusion

Adopt it if you are writing Japanese prompts for ChatGPT, Microsoft 365 Copilot or Azure OpenAI Service and want a starting structure rather than a blank page. Skip it if you need English prompts, machine-readable prompt files, or an evaluated, versioned prompt package. Before using anything from it, open the folder matching your task (会議, 営業, 人事, データ分析 and others), check the before-and-after pair, and confirm the disclaimer in README.md about not entering sensitive or confidential information, since the repository ships no filtering or redaction layer of its own.

Frequently asked questions

Official sources

1. dahatake/GenerativeAI-Prompt-Sample-Japanese on GitHub
2. Issues
3. License: MIT
4. README