Show HN: Wayfinder – A reference implementation for evaluating AI applications (github.com)

Wayfinder is a newly launched reference implementation designed to help developers build and evaluate AI applications systematically. This project evolves an AI-powered flight search application through various evaluation methodologies, including rule-based assessments, human evaluations, and the innovative use of large language models (LLMs) as evaluators. Each phase of the Wayfinder system builds upon the previous one, providing a comprehensive framework for understanding how to effectively evaluate AI performance throughout its lifecycle—from development to deployment. The significance of Wayfinder for the AI/ML community lies in its focus on critical evaluation questions, such as determining if an AI application is improving, identifying regressions after updates, and assessing subjective qualities like user helpfulness. With practical implementations integrating tools like LangSmith for interaction tracing, the platform not only streamlines the evaluation process but also facilitates scalable assessments beyond manual reviews. By releasing a repository alongside a series of instructional articles, Wayfinder aims to offer a valuable resource for engineers tackling the complexities of AI evaluation, fostering better practices within the AI development community.