Tutorials

Using LLMs to label data is fast—but is it accurate? This post shows two ways to evaluate model performance in Label Studio, with or without ground truth. Learn how to compare outputs, track accuracy and cost, and choose the right model for your workflow.

In this post, we walk through a creative use case for Label Studio’s AI Assistant and Prompts: generating rich, image-based narratives. Follow along to see how you can build a project that combines structured labeling with a touch of storytelling.

OpenAI’s new Structured Outputs feature allows you to ensure outputs conform to a defined JSON structure. In this blog, we’ll explore how to leverage this feature for various labeling tasks.

In this article, we want to demonstrate a method of curating large datasets to reduce but not remove the cost for curating a high quality medical Q&A dataset in Label Studio and fine-tuning Llama 3 on this data.

This article is part of a longer series that will teach you how to develop and optimize a question answering (QA) system using Retrieval-Augmented Generation (RAG) architecture. In this tutorial, we are going to show you how to create a generator that builds responses based on those documents.

In this introduction to our tutorial series on optimizing RAG pipelines, we'll introduce an example question answering (QA) system leveraging a Retrieval-Augmented Generation (RAG) architecture and outline three methods for optimizing your RAG pipeline utilizing Label Studio.