# Tutorials

## [Use YOLO26 with Label Studio](https://humansignal.com/blog/use-yolo26-with-label-studio-for-fast-bounding-box-pre-annotations)
Learn how to connect YOLO26 to a Label Studio project using the YOLO ML Backend so annotators can start from model predictions and focus on review and correction instead of drawing boxes from scratch.

## [How to Label Multichannel Time Series Data for AI Models](https://humansignal.com/blog/how-to-label-multichannel-time-series-data)
Learn how to visualize and annotate complex sensor or signal time series data across multiple channels within the Label Studio interface.

## [How to Automatically Disallow Text in Annotations](https://humansignal.com/blog/how-to-automatically-disallow-text-in-annotations)
Learn how you can use Plugins to create a simple content moderation interface that will alert annotators of disallowed text in task submissions.

## [How to Detect and Pause Spammy Annotations](https://humansignal.com/blog/how-to-detect-and-pause-spammy-annotations)
Learn how to automatically mitigate suspicious annotations like spam, bots, and fraud with Plugins in Label Studio Enterprise.

## [How to Generate Synthetic Data with Prompts in Label Studio](https://humansignal.com/blog/how-to-generate-synthetic-data-with-prompts-in-label-studio)
Kickstart LLM training or RAG pipelines with model-generated data. This guide shows how to create a project, write prompts, and review outputs step by step.

## [How to Evaluate and Compare LLMs Using Prompts in Label Studio](https://humansignal.com/blog/how-to-evaluate-and-compare-llms-using-prompts-in-label-studio)
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.

## [Craft the Greatest GPTs 😸](https://humansignal.com/blog/craft-the-greatest-gpts)
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.

## [Testing SmolDocling with Label Studio: Evaluating OCR for Document Conversion](https://humansignal.com/blog/testing-smoldocling-with-label-studio-evaluating-ocr-for-document-conversion)
SmolDocling is designed for end-to-end document conversion, extracting text, tables, and layout with high efficiency. But how well does it perform on real documents? In this post, we walk through testing SmolDocling’s OCR capabilities using Label Studio and a step-by-step notebook to help you evaluate its accuracy.

## [OpenAI Structured Outputs with Label Studio](https://humansignal.com/blog/openai-structured-outputs-with-label-studio)
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.

## [Fine-Tuning Llama 3: Enhancing Accuracy in Medical Q&A With LLMs](https://humansignal.com/blog/fine-tuning-llama-3-enhancing-accuracy-in-medical-q-and-a-with-llms)
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.

## [Improving RAG Document Search Quality with Cohere Re-ranking](https://humansignal.com/blog/improving-rag-document-search-quality-with-cohere-re-ranking)
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.

## [Optimizing RAG Pipelines with Label Studio](https://humansignal.com/blog/optimizing-rag-pipelines-with-label-studio)
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.