Unlocking the Power of Automated Evaluations in AI 🎯
Automated testing isn’t just beneficial; it’s essential—especially in AI engineering. To create reliable applications, we must run evaluations frequently, ideally after every code change. This post walks you through integrating evaluations into your daily workflow seamlessly.
Key Insights:
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Dynamic Testing:
- Continuous evaluation helps spot bugs immediately, ensuring high application quality.
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My Journey:
- From building a simple grocery list generator to enhancing its evaluation process.
- Leveraging pre-commit hooks and exploring asynchronous evaluations to optimize testing speed.
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Evaluation Beyond Pass/Fail:
- Established a grade system for AI outputs, focusing on correctness, language, and conciseness.
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Data-Driven Improvements:
- Analyzed trends over time against past evaluations to assure consistent quality.
Next Steps:
Ready to elevate your AI projects? Explore how Eval-Driven Development can transform your workflow!
🔗 If you found this useful, share your thoughts and experiences below! Let’s engage and innovate together! 💡
