Sprints

Sprint Retrospective – Sprint 1

Date: 28/07/2025
Sprint Duration: 15/07/2025 – 03/08/2025
Attendees: Navheen, Krittapas

Sprint Goal

Establish the foundation for the course-to-AI assistant application by setting up core frameworks, defining the data handling strategy, and initiating UI planning and development.

What Went Well

• Framework Setup Complete: Successfully set up and integrated React (frontend), FastAPI (backend), and Render (deployment).
• Clear Data Strategy: Agreed on converting course descriptions into standardized JSON format for use with LLMs.
• UI Planning & Development Started: Krittapas began implementing the frontend based on initial wireframes.
• Effective Task Division: Tasks were clearly split — Navheen focused on backend setup and data structure, while Krittapas focused on UI.
• Prototype Delivered: A working version of the prototype was completed and ready for client presentation.
• Positive Client Feedback: The client appreciated the clean UI design and overall direction of the project.

What Could Be Improved

• Time Buffer for Unexpected Issues: A minor delay occurred during the setup phase. Adding buffer time in future sprints may help avoid impact on other tasks.
• Documentation of Setup Steps: Some configurations took extra time due to lack of documentation. This can be improved in the next sprint.

What To Try Next Time

• Start Backend and Frontend Tasks in Parallel using mock data to reduce idle time.
• Document Environment Setup Clearly to ease onboarding and reduce confusion.
• Begin API Integration Earlier to allow more time for handling edge cases.

Sprint Outcome

Deliverable	Status
React, FastAPI, and Render configured	✅ Completed
Course data structuring strategy defined	✅ Completed
Initial frontend UI implemented	✅ Completed
Functional prototype delivered	✅ Completed
Client presentation and feedback session	✅ Completed

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Sprint Plan – Sprint 2

Date: 11/08/2025
Sprint Duration: 04/08/2025 – 17/08/2025
Attendees: Navheen, Krittapas

Sprint Goal

Sprint Goal

Enhance the application by implementing sentence transformation features, refining backend data handling, improving frontend UI/UX, and deploying the system on Render for hosting.

What Went Well

• Backend Progress: Navheen successfully worked on sentence transforming and data updates, improving backend structure for future scalability.
• Frontend Enhancements: Krittapas delivered noticeable UI improvements, making navigation and layout more consistent and polished.
• Hosting Research Completed: Render was researched as a deployment option, providing clarity on pricing, setup, and suitability.
• Task Continuity: Clear handoff of responsibilities each week ensured no blockers between backend and frontend tasks.
• Deployment Initiated: Navheen began deployment of the system on Render, moving the project closer to live testing.

What Could Be Improved

• Timeline Management: Some tasks stretched across multiple meetings (sentence transforming, hosting research) — setting smaller milestones could help.
• Vector Visualization Planning: The color-coded vector logic was only defined late in the sprint, leaving limited time for implementation.
• Testing Coverage: Limited focus on testing backend and frontend features before deployment.

What To Try Next Time

• Break Large Tasks into Milestones (e.g., backend updates → API endpoint updates → integration testing).
• Introduce Testing Early to validate both backend and frontend changes before deployment.
• Finalize Visualization Designs Earlier so implementation doesn’t bottleneck near the sprint end.

Sprint Outcome

Deliverable	Status
Backend sentence transforming implemented	✅ Completed
Backend data updates for integration	✅ Completed
Frontend UI/UX improvements	✅ Completed
Render hosting research	✅ Completed
Initial deployment to Render	✅ Completed
Color-coded sentence transformer vector logic	✅ Defined (Implementation In Progress)

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