Show HN: Pi Labs – AI scoring and optimization tools for software engineers
📰 Hacker News · achintms
Learn how Pi Labs' AI scoring and optimization tools can help software engineers build high-performance AI and search apps
Action Steps
- Build a scoring system to encapsulate metrics for AI application performance
- Configure optimizers to compile against the scorer, including prompt optimization and data filtering
- Calibrate the scoring system using developer, user, or rater feedback
- Test and recompile optimizers after updating the scoring system
- Apply Pi Labs' architecture to existing AI and search applications to improve performance
Who Needs to Know This
Software engineers and developers can benefit from Pi Labs' tools to improve the performance and sophistication of their AI and search applications. The tools can also be used by data scientists and machine learning engineers to optimize and fine-tune their models.
Key Insight
💡 Pi Labs' architecture allows for automatic updating of optimizers when the scoring system changes, making it easier to build and improve AI and search applications
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🚀 Pi Labs' AI scoring and optimization tools help software engineers build high-performance AI and search apps! 🤖
Key Takeaways
Learn how Pi Labs' AI scoring and optimization tools can help software engineers build high-performance AI and search apps
Full Article
Hey HN, after years building some of the core AI and NLU systems in Google Search, we decided to leave and build outside. Our goal was to put the advanced ML and DS techniques we’ve been using in the hands of all software engineers, so that everyone can build AI and Search apps at the same level of performance and sophistication as the big labs. This was a hard technical challenge but we were very inspired by the MVC architecture for Web development. The intuition there was that when a data model changes, its view would get auto-updated. We built a similar architecture for AI. On one side is a scoring system, which encapsulates in a set of metrics what’s good about the AI application. On the other side is a set of optimizers that “compile” against this scorer - prompt optimization, data filtering, synthetic data generation, supervised learning, RL, etc. The scoring system can be calibrated using developer, user or rater feedback, and once it’s updated, all the optimizers get recompiled
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