An Implementation of Fully Traced and Evaluated Local LLM Pipeline Using Opik for Transparent, Measurable, and Reproducible AI Workflows

A new workflow utilizing Opik enables comprehensive building, tracing, and evaluation of large language model (LLM) pipelines, emphasizing transparency, reproducibility, and measurable performance. The tutorial demonstrates how to integrate lightweight models, prompt-based planning, dataset creation, and automated evaluation, with Opik providing detailed function span tracking, visualization, and quality metrics to facilitate debugging, extension, and comparison of AI systems.