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USE-OF-AI.md

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+## Use of AI Tools
+
+Throughout this mini-challenge, our team leveraged AI tools, specifically ChatGPT and GitHub Copilot, to assist with the development and optimization of our sentiment analysis system. These tools were instrumental in managing coding and debugging tasks, allowing us to focus on the core challenge of implementing and evaluating weak labeling strategies to improve model performance with limited labeled data. Here's a detailed look at how we utilized these AI tools and the strategies that proved most effective.
+
+### ChatGPT
+
+ChatGPT served as a vital support tool for our software engineering tasks. Although it couldn't directly influence conceptual decisions due to its training data limitations, it was incredibly useful for generating well-structured code and solving programming issues.
+
+**How We Used ChatGPT:**
+
+- **Specific Technical Questions**: We frequently asked ChatGPT detailed technical questions to help generate efficient and clean code. For instance, asking, "How can I create a modular class structure for processing sentiment data?" resulted in well-organized code suggestions that improved our project’s architecture.
+- **Debugging Assistance**: ChatGPT was invaluable for troubleshooting. When encountering bugs, we described the problems in detail, such as "ChatGPT, why is this data preprocessing function failing with certain input formats?" This approach helped us quickly identify and fix issues, ensuring smoother progress.
+
+### GitHub Copilot
+
+GitHub Copilot significantly accelerated our development process by handling boilerplate and utility code directly within our IDE. This integration allowed us to concentrate on the more complex aspects of the sentiment analysis system, particularly the weak labeling techniques.
+
+**How We Used GitHub Copilot:**
+
+- **Inline Code Generation**: We relied on Copilot to generate code snippets as we typed. For example, while writing a function to generate text embeddings, we would start with the function signature and let Copilot complete the body. This approach saved time and reduced manual coding effort.
+- **Code Refactoring**: Copilot was also effective for optimizing existing code. By adding comments like `# Refactor this code for efficiency`, we received improved code suggestions that enhanced readability and performance.
+
+### Evaluation and Impact
+
+The integration of ChatGPT and GitHub Copilot greatly improved our development efficiency. These tools allowed us to automate routine coding tasks and focus our efforts on the strategic implementation of weak labeling techniques. As a result, we could efficiently explore and refine our approaches, ultimately boosting the performance of our sentiment classification model despite the initial scarcity of labeled data. The use of AI tools not only sped up development but also ensured that our codebase remained clean, maintainable, and well-documented, contributing significantly to the overall success of the project.