Open-source LLM
tracing and evaluation __

"Just came across Arize-phoenix, a new library for LLMs and RNs that provides visual clustering and model interpretability. Super useful."

"I always get asked what my favorite **Open Source** eval tool is. The most impressive one thus far to me is Phoenix."

"As LLM-powered applications increase in sophistication and new use cases emerge, deeper capabilities around LLM observability are needed to help debug and troubleshoot. We’re pleased to see this open-source solution from Arize, along with a one-click integration to LlamaIndex, and recommend any AI engineers or developers building with LlamaIndex check it out."

"This is something that I was wanting to build at some point in the future, so I’m really happy to not have to build it. This is amazing."

"Arize's Phoenix tool uses one LLM to evaluate another for relevance, toxicity, and quality of responses. The tool uses "Traces" to record the paths taken by LLM requests (made by an application or end user) as they propagate through multiple steps. An accompanying OpenInference specification uses telemetry data to understand the execution of LLMs and the surrounding application context. In short, it's possible to figure out where an LLM workflow broke or troubleshoot problems related to retrieval and tool execution."

"Large language models...remain susceptible to hallucination — in other words, producing false or misleading results. Phoenix, announced today at Arize AI’s Observe 2023 summit, targets this exact problem by visualizing complex LLM decision-making and flagging when and where models fail, go wrong, give poor responses or incorrectly generalize."

"Phoenix is a much-appreciated advancement in model observability and production. The integration of observability utilities directly into the development process not only saves time but encourages model development and production teams to actively think about model use and ongoing improvements before releasing to production. This is a big win for management of the model lifecycle."

"Phoenix integrated into our team’s existing data science workflows and enabled the exploration of unstructured text data to identify root causes of unexpected user inputs, problematic LLM responses, and gaps in our knowledge base."

"We are in an exciting time for AI technology including LLMs. We will need better tools to understand and monitor an LLM’s decision making. With Phoenix, Arize is offering an open source way to do exactly just that in a nifty library."