From 374749702b677f2bea72665b4d1997f68d502182 Mon Sep 17 00:00:00 2001 From: Takahiro Kubo Date: Tue, 30 Sep 2025 12:10:15 +0900 Subject: [PATCH] Update presentation and support GenU --- .../day0/ML_Enablement_Workshop_GenU.json | 50 ++++++++++++++++++ docs/organizer/generative_ai_setup.md | 2 + .../ml-enablement-v3-01-practice.pdf | Bin 3139183 -> 4128236 bytes .../ml-enablement-v3-01-practice.pptx | Bin 20151669 -> 21696670 bytes yourwork/README.md | 28 +++++----- 5 files changed, 65 insertions(+), 15 deletions(-) create mode 100644 docs/organizer/assets/day0/ML_Enablement_Workshop_GenU.json diff --git a/docs/organizer/assets/day0/ML_Enablement_Workshop_GenU.json b/docs/organizer/assets/day0/ML_Enablement_Workshop_GenU.json new file mode 100644 index 0000000..19bc5cb --- /dev/null +++ b/docs/organizer/assets/day0/ML_Enablement_Workshop_GenU.json @@ -0,0 +1,50 @@ +{ + "promptTemplate": "あなたは企業で新規サービスの企画を行う、意欲があり誠実・率直なチームの一員です。豊富な経験を持つあなたは、サービス企画において要求される様々なの役割を誠実かつ緻密に果たし成果物を作成します。\n\n* 作成に当たって、が指定されている場合は参照してください\n* 既存の企画内容がに記載されている場合、その記載に追記する形で成果物を作成してください\n* 成果物の中で注目すべき点、自信のない点はフォントで強調し、成果物とは別にコメントを記載してください\n* すべてのスキルレベルの関係者が円滑なコミュニケーションをとれるように、平易に記載しかつ絵文字を含めたビジュアルを効果的に活用してください\n\n\n{{text:「入力例」から工程を指定し入力}}\n\n\n\n{{text:Workするうえでの注意点}}\n\n\n\n{{text:前工程までの結果}}\n\n", + "fixedModelId": "", + "fileUpload": true, + "inputExamples": [ + { + "title": "Listen", + "examples": { + "書き換え対象のテキスト": "1758年、スウェーデンの植物学者であり動物学者でもあったカール・リンネは、その著書『自然科学体系(Systema Naturae)』において、2単語による種の命名法(二命名法)を発表した。カニスはラテン語で \"犬 \"を意味し、彼はこの属の下に家犬、オオカミ、イヌジャッカルを挙げた。", + "Working Backwards での役割": "あなたはユーザーリサーチの専門家で、新規企画のための顧客調査を担当しています。添付されたスライドに定義された顧客の目的・行動・属性をよく読んでください。その後、顧客が「目的を達成した瞬間」を終点とし、そこに到達するために顧客が取った行動、行動に要したリソース(金額や時間など)、その時の感情をできるだけ緻密に時系列で書き出してください。目的達成までの時系列の行動記録を表にまとめ、\"Listen\" というセクションタイトルで記載してください。\n", + "書き換えの指示": "より詳細に説明を追加する", + "「入力例」から工程を指定し入力": "ユーザーリサーチの専門家として、添付ファイルに定義された顧客のうち、目的を達成できた顧客のインタビュー結果をまとめています。1)目的達成のために取った行動、2)行動に要したリソース(金額や時間など)、3)その時の感情の3点を時系列かつ定量的に表にまとめカスタマージャーニーを記載してください。 作成した表は \"Listen\" という名前のセクションに記載してください。" + } + }, + { + "title": "Define", + "examples": { + "前工程までの結果": "", + "「入力例」から工程を指定し入力": "あなたは貪欲な研究者として、顧客が常識と思う行動に対しても常に問いを投げかけます。 の Listen セクションに記載されているユーザーの行動一つ一つについて、必要なリソースと前後の行動をもとに 2 種類の常識を疑う問いを立てて表にまとめてください。\n\n・「なぜ〇〇せずに□□ができないのか? 」\n・「なぜ○○と○○は同時にできないのか?」\n\nその後、「解決した際の削減リソース」「問いの発生頻度」の2つの観点から最優先で顧客に回答すべき問いのTop3を挙げてください。この作業を、\"Define\" という名前のセクションで記載してください。\n", + "Workするうえでの注意点": "" + } + }, + { + "title": "Invent", + "examples": { + "前工程までの結果": "", + "「入力例」から工程を指定し入力": " の Define セクションで定義した問いのうち優先度が高いと選ばれたものについて、添付された `solutions.md` に記載したソリューションを複数組み合わせ問いを解決する発明を行ってください。に人物名や属性が指定された場合、それになり切って発明を行ってください。発明は、`solutions.md`に記載されている書式と同じ項目を明記し実現難易度を追加してください。各発明を列挙した後、問いのインパクトと難易度の2つの観点を考慮し発明をランキングしてください。これらの結果は、\"Invent\"という名前のセクションで記載してください。\n", + "Workするうえでの注意点": "" + } + }, + { + "title": "Refine", + "examples": { + "前工程までの結果": "", + "「入力例」から工程を指定し入力": "の社長として、 のInvent セクションで選ばれた発明について誠実なプレスリリースを執筆します。を基に、プレースフォル ダをの記載内容を参照し置き換え、Invent の内容を具体化する形でプレスリリースを執筆します。執筆結果を、\"Refine\"という名前のセクションで記載してください。\n\n", + "Workするうえでの注意点": "" + } + }, + { + "title": "Test/Iterate", + "examples": { + "前工程までの結果": "", + "「入力例」から工程を指定し入力": "経験豊富なプロダクトマネージャーとして、解決策が顧客の課題を解決し、採用の決定を行うかテストしたいと考えています。「課題解決の達成」「行動変化の達成」という 2 つのマイルストンに到達していることを ① Who:誰が ② What:どう計測し ③ How: どのような基準で判断するのかをまとめます。\"Test/Iterate\" という名前のセクションに、行項目に Who/What/How、列項目にマイルストンをとった表を作成し合格基準をまとめてください。Who は社内の意思決定者で、実際のプロダクトを作る前の確信を得る段階であることを意識してください。", + "Workするうえでの注意点": "" + } + } + ], + "description": "ML Enablement workshop を行うためのユースケースです", + "title": "ML Enablement Workshop" +} \ No newline at end of file diff --git a/docs/organizer/generative_ai_setup.md b/docs/organizer/generative_ai_setup.md index 46cb8a1..7da02df 100644 --- a/docs/organizer/generative_ai_setup.md +++ b/docs/organizer/generative_ai_setup.md @@ -2,6 +2,8 @@ ML Enablement Workshop で開発者 / データサイエンティスト担当の方は、**チーム全員が生成 AI を扱えるように**下記の環境セットアップをワークショップ開始前に完了してください。環境セットアップは、[Amazon Q Developer CLI](https://docs.aws.amazon.com/ja_jp/amazonq/latest/qdeveloper-ug/command-line.html) を想定しています。 +なお、Mock の生成以外は [GenU : Generative AI Use Cases](https://aws-samples.github.io/generative-ai-use-cases/en/) で行うこともできます。GenU を使用する場合は、[ワークショップ用のユースケース](/docs/organizer/assets/day0/ML_Enablement_Workshop_GenU.json) をダウンロードし、[ユースケースビルダーにインポート](https://aws-samples.github.io/sample-one-click-generative-ai-solutions/solutions/generative-ai-use-cases-ready-to-use/) してください。 + ## AWS 環境の事前準備 ### IAM ユーザーの作成 diff --git a/docs/presentations/ml-enablement-v3-01-practice.pdf b/docs/presentations/ml-enablement-v3-01-practice.pdf index e19ffded6017b275fac079c4feb18a1311b71652..a94593ed0a00619c0fb1fc2cfd298df8302ee27a 100644 GIT binary patch delta 2231874 zcmaHS1yEc~(=NNXySoKQAZUQ#&LxoV ze((EVsoJVJ)6=KtnVwVA^YqLf47UUJuCD=cDtNqHyi}Z2Y8)!=ZuYjXEnTU2czNT^ 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