How To Use Annotations To Collect Human Feedback On Your LLM Application

Phoenix is an AI development platform that allows users to collect human feedback on their Large Language Model (LLM) applications, making it easier to evaluate and improve these models. The platform provides a robust system for capturing and cataloging human annotations, which can be added via the UI or through SDKs or API. Annotations can be used to create datasets, log user feedback in real-time, and filter spans and traces in the UI or programmatically. Phoenix also integrates with its Datasets feature, allowing users to fine-tune their models using annotated data. The platform enables a new system of collecting human feedback en masse through reinforcement learning from human feedback (RLHF), popularized by the rise of LLM-based evaluations. With Phoenix, users can now log human feedback into their applications, combining automated metrics with human insights to create models that not only perform well but also resonate with users.

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