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6 | 6 | ## UX Design Process
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7 | 7 | - **Wireframes:**
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8 | 8 | - [Attach or link to wireframes used in the design process.]
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9 |
| - - [Briefly explain the rationale behind the layout and design choices depicted in the wireframes.] |
| 9 | + - [Explain the rationale behind the layout and design choices depicted in the wireframes.] |
10 | 10 | - **Mockups:**
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11 | 11 | - [Include or link to mockups showcasing the visual design.]
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12 | 12 | - [Summarize how the mockups reflect the intended user experience and branding.]
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|
16 | 16 | - [Explain key design decisions, such as layout, color scheme, and typography.]
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17 | 17 | - [Discuss how accessibility guidelines (e.g., WCAG) were integrated.]
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18 | 18 | - **Reasoning for Changes:**
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19 |
| - - [Summarize any significant changes made to the design during development and the reasons behind them.] |
| 19 | + - [Summarize significant changes made to the design during development and the reasons behind them.] |
20 | 20 |
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21 | 21 | ## Key Features
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22 | 22 | - **Feature 1:** [Briefly describe the implemented feature.]
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32 | 32 | - Use of environment variables for sensitive data.
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33 | 33 | - Ensured DEBUG mode is disabled in production.
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34 | 34 |
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35 |
| -## Reflection on AI Tools Usage |
| 35 | +## AI Tools Usage and Reflection |
36 | 36 |
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37 |
| -### Contributions of AI Tools |
38 |
| -1. **Code Creation:** |
39 |
| - - AI tools (e.g., GitHub Copilot) were used to generate initial drafts of models, views, and templates. Adjustments were made to align with project requirements. |
40 |
| -2. **Debugging:** |
41 |
| - - AI-assisted identification and resolution of code issues, saving significant debugging time. |
42 |
| -3. **Optimization:** |
43 |
| - - Suggestions from AI tools enhanced code readability and improved performance. |
44 |
| -4. **Testing:** |
45 |
| - - Generated unit tests for key features, with manual refinements for accuracy. |
| 37 | +### AI in Code Creation |
| 38 | +- **Use Case:** AI tools (e.g., GitHub Copilot) were used for generating initial drafts of models, views, and templates. |
| 39 | +- **Reflection:** |
| 40 | + - Strategic use of AI allowed for rapid prototyping of code components. |
| 41 | + - Outcomes: Enhanced productivity with minor manual adjustments for alignment with project goals. |
46 | 42 |
|
47 |
| -### Overall Impact |
48 |
| -- **Successes:** |
49 |
| - - Accelerated development with AI tools, especially in ideation and repetitive coding tasks. |
50 |
| - - Streamlined debugging and validation processes. |
51 |
| -- **Challenges:** |
52 |
| - - Initial AI-generated code sometimes required significant contextual adjustments. |
53 |
| -- **Workflow Improvement:** |
54 |
| - - AI tools enabled a focus on high-level design and logic while automating repetitive tasks. |
| 43 | +### AI in Debugging |
| 44 | +- **Use Case:** AI tools assisted in identifying and resolving bugs in the code. |
| 45 | +- **Reflection:** |
| 46 | + - AI significantly reduced debugging time by providing context-aware suggestions. |
| 47 | + - Key interventions included resolving logic errors and improving code readability. |
| 48 | + |
| 49 | +### AI in Performance and UX Optimization |
| 50 | +- **Use Case:** AI suggested performance improvements, including optimization of query efficiency and front-end responsiveness. |
| 51 | +- **Reflection:** |
| 52 | + - Suggestions improved application speed and user experience. |
| 53 | + - Minimal manual intervention was needed to apply AI-driven improvements. |
| 54 | + |
| 55 | +### AI in Automated Unit Testing |
| 56 | +- **Use Case:** AI (e.g., GitHub Copilot) was utilized to generate Django unit tests for application features. |
| 57 | +- **Reflection:** |
| 58 | + - Generated test cases covered CRUD operations and authentication logic. |
| 59 | + - Adjustments were made to improve test coverage and ensure accuracy. |
| 60 | + - Demonstrated understanding of AI-generated test logic and its alignment with functionality. |
| 61 | + |
| 62 | +### Overall Impact of AI |
| 63 | +- **Reflection on Workflow:** |
| 64 | + - AI tools streamlined repetitive tasks, enabling focus on high-level development. |
| 65 | + - Efficiency gains included faster debugging, comprehensive testing, and improved code quality. |
| 66 | + - Challenges included contextual adjustments to AI-generated outputs, which were resolved effectively. |
55 | 67 |
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56 | 68 | ## Testing Summary
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57 | 69 | - **Manual Testing:**
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|
67 | 79 | - [List any potential improvements or additional features for future development.]
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68 | 80 |
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69 | 81 | ## Documentation Process
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70 |
| -This README reflects the UX design process, including: |
71 |
| -1. **Wireframes, mockups, and diagrams** that showcase the initial design and the reasoning behind it. |
| 82 | +This README reflects the UX design process, deployment steps, and the development lifecycle: |
| 83 | +1. **Wireframes, mockups, and diagrams** that showcase the initial design and reasoning behind it. |
72 | 84 | 2. Changes made during development, ensuring alignment between design and implementation.
|
73 |
| -3. Final implementation details, demonstrating adherence to design principles and accessibility standards. |
| 85 | +3. AI tool usage, detailing its role in code creation, debugging, optimization, and testing. |
| 86 | +4. Final implementation details, demonstrating adherence to design principles and accessibility standards. |
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