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Dive into Part 3 of this practical case study, where we continue leveraging OpenAI's LLM to enhance the quality of manually entered work order defect reporting data.
👍 Evaluate AI Feedback Effectiveness: Learn how to assess the impact of OpenAI's coaching tips on improving work order data quality.
👍 Continuous Improvement Loop: Understand the process of testing and refining the AI response for optimal data enhancement.
👍 Craft Better Prompts: Master the art of prompt engineering with few-shot examples to guide OpenAI towards recognizing different text quality levels.
This episode focuses on the critical task of evaluating the OpenAI model's feedback on data improvement. We'll explore the techniques to test its effectiveness, ensuring that the suggestions made by the AI actually contribute positively to the quality of the defect reporting text.
Understanding and validating the effectiveness of these AI-driven recommendations is key to sustaining the reliability and efficiency of our data improvement system.
Additionally, we delve into the nuances of prompt engineering for the OpenAI model. You'll gain insights into using few-shot learning techniques to 'educate' the model about varying levels of text quality, ranging from poor to excellent. This segment is particularly beneficial as it empowers you to set and maintain high standards for the information input by technicians in new work orders and defect reports.
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⏩ Video Navigation
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🎓 Power BI Dashboard Training
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👉FREE Power BI Quick Start Course - Learn how to create your first dashboard in around 90 minutes!
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📁 Resources
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👉Access to the source PBIX files for this KZbin video:
effectivedashboards.com/cours...
👉Article series on creating a maintenance management system data model and how to address the human factors of data collection for improved data quality. (This is not just relevant to maintenance and reliability data).
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