Guide

AI benchmarks are quietly failing us. Studies reveal that data leakage, leaderboard manipulation, and misaligned incentives are inflating model performance. This blog explores four pillars of reform, governance, transparency, broad-spectrum metrics, and oversight, and outlines how enterprises can build trust through a centralized benchmark management platform.

Not every ML team looks the same and your labeling workflow shouldn’t either. In this guide, we break down three common annotation team setups and how to tailor your tools and processes to boost quality, speed, and scale.

RAG systems promise more accurate AI responses, but they often fall short due to retrieval errors, hallucinations, and incomplete answers. This post explores seven common RAG failures—from missing top-ranked documents to incorrect formatting—and provides practical solutions to improve retrieval accuracy, ranking, and response quality. Learn how to optimize your RAG system and ensure it delivers reliable, context-aware AI responses.

Mistral OCR is setting a new standard for document understanding, but how well does it perform on your data? Using Label Studio, you can evaluate its accuracy, compare outputs, and fine-tune results. In this post, we walk through the process and share a hands-on handbook to get started.

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.

Today, we're diving into Label Studio Format Converter, a handy tool that helps you encode labels into formats compatible with your favorite machine learning libraries, enhancing workflow efficiency.

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.

In the world of machine learning, data labeling plays a central role in training accurate and reliable models, and properly preparing your data for labeling sets the foundation for the success of your machine learning projects.

Learn how to build a prompt-centric workflow in Label Studio, which combines real-time prompt engineering with data labeling. The goal of this prompter is to make working with data and LLMs seamless and dynamic.

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.

With the rise of deep learning and machine learning techniques, computer vision has become essential in various applications. But how do you get that data into your AI model effectively?

Exploring how LLMs and agents can be leveraged to transform the annotation workflow, aiming for greater data labeling efficiency, accuracy, and scalability.

With their dynamic capabilities, agents can potentially adapt to the nuances of different datasets and environments, ensuring that the labeling process remains efficient regardless of the data's nature.

Our Data Scientist in Residence, Jimmy Whitaker, recently outlined how to fine-tune foundation models for AI applications. In case you missed it, here's a quick summary of the workshop.

We recently held a workshop demonstrating the powerful integration between the Segment Anything Model (SAM) and Label Studio. If you missed it, you can get the recap right here.

Hugging Face is a highly esteemed platform widely recognized as one of the most valuable resources for discovering and utilizing Language Model (LLM) technology.

We explore how Label Studio can be employed to build a Question-Answering bot trained on domain-specific knowledge.

Learn the lingo around fine-tuning LLMs. We've compiled a comprehensive glossary of terms that you should know when embarking on your LLM tuning journey.

Out of the box, LLMs can handle general tasks fairly easily. But what fine-tuning methodology should you use when you need to complete tasks that require more specialized knowledge than what LLMs typically have?

With just a few quick configuration changes, you can launch a compact Label Studio production environment for your annotation and labeling projects, all within Hugging Face.

Machine learning models are the race cars of artificial intelligence, designed to deliver fast and accurate results. But just like a race car, they need the right fuel - in this case, high-quality data that has been appropriately prepared.

Fine-tuning plays a pivotal role in harnessing LLMs for specific tasks. By exploring the process of fine-tuning and understanding its importance in LLMs, you will gain the knowledge and practical skills needed to optimize LLMs.

Learn some advanced techniques to get the most out of Label Studio and drive more efficiencies into your labeling process.

Hundreds of thousands of open source LLMs are available, and choosing one is not easy. However, there are a small number of highly capable models that we think are excellent choices today.

Ever wonder how our JSON format is generated or how to structure your JSON files to import data, pre-annotate your datasets, or work with the ML backend? Wait no further! We've got you covered with this blog post.

Segment Anything is a popular model you can use to accelerate computer vision & image labeling. Learn how to use the Segment Anything integration with Label Studio.

RLHF has enabled language models trained on a general corpus of text data to be aligned with complex human values. This article details how you can train a reward model for RLHF on your own data.

In this article we explore the profound impact of human feedback on AI and examine how powerful tools like Label Studio are catalyzing a paradigm shift in AI training methodologies.

To help you get started with data labeling, we've created this guide that outlines the best practices to ensure you label your data accurately.

How do you identify and maintain high-quality data when you’re knee-deep in a project? And what does “data quality” mean in the context of machine learning? Read more to find out!

In this article, we’ll explore what object detection is, the challenges involved in implementing it, and share a step-by-step breakdown of getting started with this exciting technique.

Evaluating GPT-4’s release through the lens of open source data science leads us to see the possibility and potential for long-term impact — but it also reiterates the underlying importance of dataset development and context provided by human feedback through processes such as reinforcement learning through human feedback (RLHF) or human-in-the-loop processes.

The Label Studio docs have a new look; with cleaner information architecture, and more ways to contribute.

In this guide, you'll learn how to set up an audio labeling project in Label Studio. Then we'll walk you through importing and labeling your data. You'll also see the many different features in Label Studio's audio labeling interface.

A machine learning model can send unintended, dangerous conversational responses. Train your models to avoid these outcomes by learning what machine learning bias is and how to minimize it.

With this guide, learn how audio classification works and how to implement it when building audio ML projects, so you can optimize your ML models and build a better overall product.

This article takes a look at audio classification and discusses the various use cases that can benefit from this technique.

When choosing an annotation tool for your audio classification project, you need to carefully study its unique features and ensure that it works well with the rest of your stack. Finding the right tool will give you the best value in your audio classification projects.

In this guide, we dig into image classification—what it means, how it works, and the main steps to help you get started.

Instituting an efficient data labeling process is the key to eliminating inaccuracies in the data fed to machine learning models. Here are some generally applicable principles that can improve the efficiency and accuracy of your data labeling process.

Some developers and data scientists just want to grab code, download a repo and go. If that’s your style, choosing a fully-featured open source sentiment tool might be right choice for you.

Learn six of the most common data labeling mistakes we see in ML projects and the fixes that can help you maintain consistent, accurate training data.

This blog covers the basics of sentiment analysis: key components, use cases, challenges, and solutions.

Learn how various types of data drift and how data drift impacts model performance, along with several examples of how data labeling can tackle data drift.

The top 10 important considerations for NLP labeling and functionality in labeling tools for natural language processing machine learning projects.