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This guide demonstrates a repeatable technique for converting a requirements analysis document into high-quality, developer-ready user stories and importable work items for tools like Azure DevOps. The goal is to create a concise, actionable backlog from a requirements file quickly and consistently, then refine stories into developer tasks suitable for sprint planning. Overview
  • Purpose: Extract user needs and features from a requirements document and turn them into SCRUM-aligned user stories.
  • Output: High-level user stories, developer-focused tasks, and an import-ready CSV for Azure DevOps (or similar boards).
  • Technologies used in the example: Flask (backend), OpenCV (image processing), React (frontend).
I pasted the requirements document into a chat with an LLM and iterated using a focused prompt. The prompt I used:
A browser screenshot of the ChatGPT interface showing a "Web App Requirements" document with sections like Constraints and Acceptance Criteria in the right pane, and a chat/conversation panel on the left.
Why this approach works
  • Speed: Using an LLM to scan the requirements and propose stories reduces manual effort and delivers a repeatable output format.
  • Consistency: The prompt enforces a single story format (As a user, I want to…, so that…), which aligns with common SCRUM templates.
  • Traceability: Grouping stories by functional area (upload, processing, UI, accessibility, security, scalability) makes it easier to map requirements to epics and tasks.
Initial high-level user stories (example output)
  • As a graphic designer, I want to upload images to the application so that I can optimize them for reduced file size.
  • As a graphic designer, I want to adjust the quality of my images so that I can control compression versus visual fidelity.
  • As a graphic designer, I want to view the modified image after quality adjustment so that I can immediately see the results of the optimization.
  • As a graphic designer, I want the application to have an intuitive and accessible user interface so that I can use it efficiently and inclusively.
These high-level stories were grouped around functional areas such as accessibility, security, and scalability to preserve context from the original requirements.
A dark-mode browser screenshot of the ChatGPT web interface displaying a document titled "High-Level User Stories" with numbered items like Image Upload, Quality Adjustment, and Image Display. The Chrome window and macOS menu bar are visible across the top.
Refining stories into developer-focused tasks High-level stories are user-centered and intentionally non-prescriptive. To convert them into actionable backlog items, refine each story into implementation tasks or acceptance-criteria-based subtasks. When supplying technical context to the model (for example, Flask, OpenCV, React), the model can produce developer-oriented tasks such as:
  • Set up a Flask server to handle image upload requests.
  • Create a Flask endpoint /upload that accepts images via POST.
  • Implement client- and server-side validation for uploaded images (file type, size limits).
  • Integrate OpenCV functions for image resizing, format conversion, and compression.
  • Build React components for file selection, preview, and asynchronous upload with progress indicators.
  • Write automated tests for upload flows and image-processing pipelines (unit and integration).
  • Add accessibility checks and keyboard navigation to UI components.
Mapping high-level stories to developer tasks helps engineering teams estimate and schedule work during sprint planning. Practical tips for CSV export and Azure DevOps import When exporting developer tasks as CSV for importing into Azure DevOps, make sure you match the importer’s expected headers and supported work item types. Common pitfalls include missing required columns, mismatched header names, and unsupported work item types. Key adjustments that solved import issues in this example:
  • Ensure CSV headers exactly match Azure DevOps expected column names (e.g., Work Item Type, Title, Assigned To, State, Priority).
  • Map all backlog entries to a supported work item type for the target project (e.g., Task, Issue, Epic). If the project only supports Task, convert all items to Task.
  • Populate required fields with valid values (e.g., set Priority to a valid numeric or enumerated priority).
Common Azure DevOps CSV import fields (example)
A computer screenshot showing an Excel workbook named "Image_Optimizer_User_Stories" with a table of user story titles, descriptions, priorities and work item types. The Excel window is overlaid on an Azure DevOps Boards sidebar.
Import troubleshooting and resolution
  • Issue: CSV import flagged incorrect header names and unsupported work item types.
    Resolution: Rename headers to exactly match Azure DevOps format and remap all items to the project-supported work item types.
  • Issue: Priority fields rejected invalid values.
    Resolution: Use valid enumerations or numeric values required by the project and re-run import.
After correcting headers and priorities, the import progressed and created work items that could be further edited, assigned, and prioritized.
A screenshot of Azure DevOps Boards showing a Queries view with a list of work items and an "Edit work items" dialog open in the center. The right details pane shows a task titled "Set up Flask server for image upload."
Result: actionable backlog ready for sprint planning With items imported to Azure DevOps, you can assign tasks, estimate, add acceptance criteria, and move items into sprint boards. The example project now has a “To Do” column populated with tasks ready for grooming and sprint assignment.
A screenshot of an Azure DevOps "Boards" view showing a list of work items for a project called "Image Optimizer," with task IDs, titles (e.g., "Design and implement intuitive UI"), assignment status, states ("To Do"), and activity dates. The left sidebar shows project navigation (Boards, Repos, Pipelines, etc.).
Next steps: concise technical specification After you have a prioritized backlog, produce a short technical specification to guide implementation. The spec should include:
  • Chosen technologies and rationale (e.g., Flask for lightweight APIs, OpenCV for image processing, React for UI).
  • Core architecture diagram or component list (API endpoints, processing pipelines, data flow).
  • Key endpoints and acceptance criteria (e.g., POST /upload accepts multipart/form-data, returns 200 and a processing job ID).
  • Integration points, scalability considerations, and security requirements (rate limits, file-size limits, allowed mime types).
  • Test strategy (unit, integration, and end-to-end tests).
References and further reading
When exporting to Azure DevOps or similar tools, verify that your CSV headers exactly match the importer’s expected column names and that all required fields (e.g., Work Item Type, Title, State, Priority) contain valid values. If your project only supports specific work item types, map your entries accordingly (e.g., convert everything to Task).
Summary
  • Use a precise prompt to extract structured user stories from requirements.
  • Group stories by functional area to retain context and enable epics.
  • Refine user stories into developer tasks with tech stack context (Flask, OpenCV, React) so engineering can estimate and implement.
  • Prepare a correctly formatted CSV for import to Azure DevOps by matching headers and supported work item types.
  • Produce a short technical spec that includes endpoints, acceptance criteria, and integration notes to complete the handoff for implementation.

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