Guide

Learn about Reinforcement Learning with Verifiable Rewards, one of the leading training strategies for injecting learning signals into LLMs, successfully employed by models such as DeepSeek R1 and Tülu 3.

Get started with content moderation labeling to classify potentially harmful data for training purposes, or evaluate the output of a model you already have in place.

Evaluating multi-turn conversations with Label Studio helps identify performance gaps, refine responses, and improve user experience for a more effective virtual assistant.

Go through the entire fine-tuning process on OpenAI’s platform—from preparing recently-posted Wikipedia data to estimating costs and deploying your fine-tuned model.

Learn when you should use webhooks vs. the API in Label Studio, and see examples of what you can do with webhooks.

Learn how to use Ultralytics YOLOv8 object detection model with Label Studio.

Explore the topic of evaluation for LLMs, its importance, and how we should approach it. Learn how integrating systematic evaluations can help teams iteratively refine their models to meet real-world needs.

In this tutorial, we'll guide you through the process of setting up and using Label Studio in combination with Ragas (Retrieval-Augmented Generation Answer Scoring) and GPT-4 to build an optimized QA application.

Implementing RAG-based systems comes with challenges to be aware of, particularly in assessing the quality of generated responses. This article will walk you through some of those challenges.

Delve into three effective methods to automate your labeling using Label Studio, including examples and resources.

An ongoing challenge for Large Language Models (LLMs) is their tendency to hallucinate. In this article, we explore four methods to automatically detect these errors.

The short answer is: it depends. Read on as we explore this topic further, uncovering the advantages and drawbacks of each approach to help you make an informed decision.

This post will take you through the intricacies of data quality, the strategies employed to build top-tier datasets, and how to use Label Studio Enterprise to engineer your AI/ML success.

Understanding the distinction between regular datasets and ground truth datasets is crucial for leveraging data effectively in machine learning and data analysis tasks. This article explores both concepts and digs deeper into the importance of ground truth datasets.

Generalist models, like GLiNER, provide an excellent starting point for the tasks that they aim to solve. Fine-tuning these models offers us a way to improve their performance in the areas that we care about to solve business problems.

Sure, benchmarks are cool, but they don’t give you the feel or the intuition of how a model actually works. To get that, you’ve got to hack around with the model and throw real-world prompts at it — like you’d do in day-to-day tasks.

These five tips for using Label Studio's API and SDK demonstrate these tools' powerful capabilities and flexibility for managing data labeling projects. From efficient project creation and task imports to advanced configurations and bulk data exports, Label Studio provides a comprehensive and streamlined approach suitable for beginners and advanced users.

Get a brief overview of RAG and how it relates to LLMs, learn when you might consider using RAG, and get a summary of some challenges based on current research you should be aware of should you choose to travel down this path.

From precise disease diagnoses to personalized treatment plans, accurately labeled data profoundly impacts healthcare. This guide explores the fundamentals of medical data labeling, its applications, and its evolution through AI.

When training Large Language Models and utilizing machine learning, the significance of precise and efficient data labeling cannot be overstated. Here are ten actionable tips to elevate your data labeling processes.

The realm of data labeling is undergoing significant transformations, reflecting the dynamic nature of the tech industry. Here are some of the most notable trends and their implications.

Integrating a machine learning (ML) backend into the data labeling process for a labeling platform can significantly enhance the efficiency and accuracy of the process.