Thanks to John Gilhuly for his contributions to this piece. Liking Phoenix? Please consider giving us a star on Github! ⭐️ Synthetic datasets are artificially created datasets that are designed to mimic real-world information. Unlike naturally occurring data, which is gathered from actual events or interactions, synthetic datasets are generated using algorithms, rules, or other…
Category: Large Language Models
Phoenix supports multi-modal evaluation and tracing. In this tutorial, we’ll take advantage of that to walk through the process of setting up an image classification experiment using Phoenix. This involves uploading a dataset, creating an experiment to classify the images, and evaluating the model’s accuracy. We’ll be using OpenAI’s GPT-4o-mini model for the classification task. …
Last week, LlamaIndex released Workflows, a new approach to easily create agents. Workflows use an event-based architecture instead of the directed acyclic graph approach used by traditional pipelines or chains. This new approach brings with it new considerations for developers looking to create agentic systems, as well as new questions on how to evaluate and…
With Arize Phoenix, getting started is relatively straightforward because you can run it locally and start iterating quickly during the development and experiment phases of building an LLM application. However, once you’re ready for production — or if you want to collaborate with your teammates — it’s time to deploy Phoenix. In addition to a…
Function calling is an essential part of any AI engineer’s toolkit, enabling builders to enhance a model’s utility at specific tasks. As more LLM applications leveraging tool calls get deployed into production, the task of effectively evaluating their performance in LLM pipelines becomes more critical. What Is Function Calling In AI? First launched by OpenAI…
Due to the black box nature of LLMs and the importance of tasks they’re being trusted to handle, intelligent monitoring and optimization tools are essential to ensure they operate efficiently and effectively. The integration of Arize Phoenix with LlamaIndex’s newly released instrumentation module offers developers unprecedented power to fine-tune performance, diagnose issues, and enhance the…
Recently, I attended a workshop organized by Arize AI titled “RAG Time! Evaluate RAG with LLM Evals and Benchmarking.” Hosted by Amber Roberts – ML Growth Lead at Arize AI, and Mikyo King – Head of Open Source at Arize AI, the talks provided valuable insights into an important field of study. Miss the event?…
This article is co-authored by Mikyo King, Founding Engineer and Head of Open Source at Arize AI, and Xander Song, AI Engineer at Arize AI Building a baseline for a RAG pipeline is not usually difficult, but enhancing it to make it suitable for production and ensuring the quality of your responses is almost always…
This piece is co-authored by Roger Yang, Software Engineer at Arize AI Observability in third-party large language models (LLMs) is largely approached with benchmarking and evaluations since models like Anthropic’s Claude, OpenAI’s GPT models, and Google’s PaLM 2 are proprietary. In this blog post, we benchmark OpenAI’s GPT models with function calling and explanations against…
What is LLM App Tracing? The rise of large language model (LLM) application development has enabled developers to move quickly in building applications powered by LLMs. The abstractions created by these frameworks can accelerate development, but also make it hard to debug an LLM app. This is where Arize Phoenix, a popular open-source library for…