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Thank you very much @iusztinpaul for this excellent course and high-quality project.
It stands out as one of the few comprehensive end-to-end Machine Learning projects that effectively demonstrates the entire workflow using state-of-the-art techniques and production-grade libraries.
To manage rate limits, you manually increased the wait time between API calls upon hitting the request threshold, like in the __get_quality_score_batch method of the QualityScoreAgent class.
Have you considered implementing exponential backoff for retries, as recommended in the OpenAI API documentation, instead of relying on fixed delays?