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Get the full picture of your model's performance. Log inputs and outputs and seamlessly enrich them with metadata and user feedback.

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Analyze model performance

Figure out how your model is really working, and where you can improve. Monitor for errors and discover underperforming cohorts and use cases.

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Impr... See more

Here’s how the LinkedIn Engineering team reduced the error rate from ~10% to ~0.01% when generating structured outputs with LLMs. Turning natural text into structured outputs is a cool use case for LLMs. But getting LLMs to do this reliably is quite challenging. https://t.co/FOgEWE4K3Y

"Best Practices and Lessons Learned on Synthetic Data for Language Models" Recent Paper from Google DeepMind 📌 The paper addresses the creation and application of multimodal synthetic data, particularly in vision-to-text tasks. Projects like Pix2Struct and MatCha showcase the generation of ... See more