{"id":1775,"date":"2026-02-19T04:31:07","date_gmt":"2026-02-19T04:31:07","guid":{"rendered":"https:\/\/askeladden.in\/?page_id=1775"},"modified":"2026-02-19T04:37:30","modified_gmt":"2026-02-19T04:37:30","slug":"ask026-ai-generation-of-user-stories","status":"publish","type":"page","link":"https:\/\/askeladden.in\/?page_id=1775","title":{"rendered":"ASK026: AI Generation of User Stories"},"content":{"rendered":"\n
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Objectives<\/h3>\n\n\n\n
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  • Review the use of AI to generate user stories for Agile Development.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n
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    How to Generate User Stories from Data, Docs & Diagrams<\/h2>\n\n\n\n

    Combining the context-heavy power of NotebookLM<\/strong> with the creative, structured output of Gemini<\/strong> is a brilliant way to automate the heavy lifting of product management. Since NotebookLM excels at synthesizing your existing documents and Gemini excels at formatting, you can create a seamless “data-to-story” pipeline.<\/p>\n\n\n\n

    Here is a step-by-step guide to building your Agile User Story factory.<\/p>\n\n\n\n

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    1. Prepare your Source Material (NotebookLM)<\/h2>\n\n\n\n

    First, upload all your disparate files into a single NotebookLM<\/strong> notebook. This allows the AI to “ground” its logic in your specific business rules rather than generalities.<\/p>\n\n\n\n

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    • Data Feeds:<\/strong> Upload your JSON\/CSV samples.<\/li>\n\n\n\n
    • Functional Docs:<\/strong> Upload your Word\/PDF requirements.<\/li>\n\n\n\n
    • Calculations:<\/strong> Upload your Excel\/Sheets files (NotebookLM will parse the logic and formulas).<\/li>\n\n\n\n
    • UI Mockups:<\/strong> Export your Draw.io files as PDFs or high-res PNGs<\/strong> and upload them. NotebookLM can “read” the text and layout within these images.<\/li>\n<\/ul>\n\n\n\n

      Pro-Tip:<\/strong> Use the “Notebook Guide” feature in NotebookLM to generate a Brief<\/strong>. Copy this brief\u2014it serves as the perfect high-level summary to give Gemini context.<\/p>\n\n\n\n

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      2. The Gemini “Agile Architect” Prompt<\/h2>\n\n\n\n

      Once NotebookLM has synthesized the data, move over to Gemini to generate the formal stories. Use a prompt that forces the AI to cross-reference all four of your data types.<\/p>\n\n\n\n

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      The Prompt:<\/strong><\/p>\n\n\n\n

      “I am building [Project Name]. Based on the following context [Paste NotebookLM Brief here], please generate a set of Agile User Stories.<\/p>\n\n\n\n

      For each story, you must incorporate:<\/strong><\/p>\n\n\n\n

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      1. Data:<\/strong> Reference the specific fields from the sample feeds.<\/li>\n\n\n\n
      2. Logic:<\/strong> Include the specific calculation requirements from the Sheets data.<\/li>\n\n\n\n
      3. UI:<\/strong> Describe the interface elements based on the Draw.io mockups.<\/li>\n<\/ol>\n\n\n\n

        Format:<\/strong> > * User Story:<\/strong> As a [role], I want to [action], so that [value].<\/p>\n\n\n\n

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        • Acceptance Criteria:<\/strong> (Numbered list including technical constraints).<\/li>\n\n\n\n
        • Technical Notes:<\/strong> (Data mapping and formula references).”<\/li>\n<\/ul>\n<\/blockquote>\n\n\n\n
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          3. Example Output<\/h2>\n\n\n\n

          Here is how Gemini might synthesize those specific inputs into a single story:<\/p>\n\n\n\n

          Story: Real-Time Margin Calculator<\/strong><\/h3>\n\n\n\n

          User Story:<\/strong> As a Portfolio Manager<\/strong>, I want to see a live margin-call estimate<\/strong> on the dashboard, so that I can hedge my positions before market close.<\/p>\n\n\n\n

          Acceptance Criteria:<\/strong><\/p>\n\n\n\n

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          1. The UI must display a “Risk Gauge” as seen in the draw.io<\/code> mockup (Top Right Quadrant).<\/li>\n\n\n\n
          2. The calculation must use the IMPLIED_VOL<\/code> field from the JSON Data Feed<\/strong>.<\/li>\n\n\n\n
          3. The margin result must trigger a “Red” state if the value exceeds the THRESHOLD_VAR<\/code> defined in the Logic Sheet (Cell B12)<\/strong>.<\/li>\n\n\n\n
          4. Data must refresh every 30 seconds.<\/li>\n<\/ol>\n\n\n\n

            Technical Notes:<\/strong><\/p>\n\n\n\n

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            • Source Logic:<\/strong> $Margin = (Price \\times Quantity) \\times \\text{Risk\\_Factor}$<\/li>\n\n\n\n
            • UI Element:<\/strong> Uses the “Primary Action Button” styling from the wireframe.<\/li>\n<\/ul>\n\n\n\n
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              4. Best Practices for Accuracy<\/h2>\n\n\n\n
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              • Verify Calculations:<\/strong> NotebookLM is great at explaining formulas, but always double-check the math. If a calculation is $A + B = C$, tell Gemini explicitly: “Use the formula found in the ‘Global Logic’ document.”<\/li>\n\n\n\n
              • The “Context Window” Advantage:<\/strong> Because you are using NotebookLM as the “brain,” you don’t need to paste 50 pages of text into Gemini. Just paste the Summary<\/strong> or specific Source Citations<\/strong> provided by NotebookLM.<\/li>\n\n\n\n
              • Iterate on UI:<\/strong> If the Draw.io mockup is complex, ask NotebookLM: “List all the buttons and input fields shown in the UI mockup PDF.”<\/em> Then, paste that list into Gemini to ensure every button gets a user story.<\/li>\n<\/ul>\n\n\n\n

                Would you like me to draft a custom system prompt you can save and reuse for these specific data types?<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

                Objectives How to Generate User Stories from Data, Docs & Diagrams Combining the context-heavy power of NotebookLM with the creative, structured output of Gemini is a brilliant way to automate the heavy lifting of product management. Since NotebookLM excels at synthesizing your existing documents and Gemini excels at formatting, you can create a seamless “data-to-story” […]<\/p>\n","protected":false},"author":1,"featured_media":1779,"parent":27,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-1775","page","type-page","status-publish","has-post-thumbnail","hentry","category-project"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t