NEW10X Faster Labeling with Prompts—Now Generally Available in SaaS

Open Source
Data Labeling Platform

The most flexible data labeling platform to fine-tune LLMs, prepare training data or validate AI models.

Last Commit: May 6, 2025

Latest version: nightly


                # Install the package
# into python virtual environment
pip install -U label-studio# Launch it!label-studio

Label every data type.

GenAI

LLM Fine-Tuning

Label data for supervised fine-tuning or refine models using RLHF

LLM Evaluations

Response moderation, grading, and side-by-side comparison

RAG Evaluation

Use Ragas scores and human feedback

Flexible and configurable

Configurable layouts and templates adapt to your dataset and workflow.

Integrate with your ML/AI pipeline

Webhooks, Python SDK and API allow you to authenticate, create projects, import tasks, manage model predictions, and more.

ML-assisted labeling

Save time by using predictions to assist your labeling process with ML backend integration.

Connect your cloud storage

Connect to cloud object storage and label data there directly with S3 and GCP.

Explore & understand your data

Prepare and manage your dataset in our Data Manager using advanced filters.

Multiple projects and users

Support multiple projects, use cases and data types in one platform.

From the Blog

View All Articles
  • How to Generate Synthetic Data with Prompts in Label Studio

    Learn how to generate synthetic Q&A pairs using Label Studio’s Prompts feature. This step-by-step guide walks through creating a project, writing prompts, and reviewing model-generated data—perfect for bootstrapping RAG systems or training LLMs when labeled data is limited.

    Micaela Kaplan

    April 23, 2025

  • Why Human Review is Essential for Better RAG Systems

    Automated metrics can tell you what’s wrong with your RAG system—human review tells you why.
    This post walks through a structured approach to evaluating RAG outputs using tools like Ragas and Label Studio. Learn how to prioritize weak responses, streamline human-in-the-loop review, and use feedback to iteratively improve your retrieval, prompts, and models.

    Jimmy Whitaker

    April 11, 2025

  • Building a RAG System with Label Studio

    Training a RAG system starts with clean, structured QA data—but messy source material makes that tough. In this post, we share how we used Label Studio and its Prompts feature to break down tasks, synthesize QA pairs, and build a reliable RAG assistant. If you're looking to scale RAG with better data and smarter workflows, this guide is for you.

    Max Tkachenko

    April 10, 2025

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