SAN FRANCISCO, Aug. 30, 2023 /PRNewswire/ — Arize AI, a market leader in machine learning observability, debuted industry-first capabilities for troubleshooting large language models (LLMs) at Google Cloud Next ’23 today.
Arize’s new prompt engineering workflows, including a new prompt playground, enables teams to find prompt templates that need to be improved, iterate on them in real time, and verify improved LLM outputs.
Prompt analysis is an important component in troubleshooting an LLM’s performance. Often, LLM performance can be improved simply by testing different prompt templates, or iterating on one to achieve better responses.
With these new workflows, teams can:
Uncover responses with poor user feedback or evaluation scoresIdentify the template associated with poor responsesIterate on the existing prompt templateCompare responses across prompt templates in a prompt playground
Arize is also launching additional search and retrieval workflows to help teams using retrieval augmented generation (RAG) troubleshoot where and how the retrieval needs to be improved. These new workflows will help teams identify where they may need to add additional context into their knowledge base (or vector database), when the retrieval didn’t retrieve the most relevant information, and ultimately understand why their LLM may have hallucinated or generated sub-optimal responses.
“Building LLM-powered systems that responsibly work in the real-world is still too difficult today,” said Aparna Dhinakaran, Co-Founder and Chief Product Officer of Arize. “These industry-first prompt engineering and RAG workflows will help teams get to value and resolve issues faster, ultimately improving outcomes and proving the value of generative AI and foundation models across industries.”
About Arize AI
Arize AI is a machine learning observability platform that helps ML teams deliver and maintain more successful AI in production. Arize’s automated model monitoring and observability platform allows ML teams to quickly detect issues when they emerge, troubleshoot why they happened, and improve overall model performance across both structured data and image and large language models. Arize is a remote-first company with headquarters in Berkeley, CA.
Media Contact: Krystal Kirkland, press@arize.com
View original content:https://www.prnewswire.com/news-releases/arize-ai-unveils-prompt-engineering-and-retrieval-tracing-workflows-for-llm-troubleshooting-301914056.html
SOURCE Arize AI