Create a Prompt

Prerequisites

• An API key for your LLM.
• A project that meets the criteria noted below.

Model provider API keys

You can specify one OpenAI API key and/or multiple custom and Azure OpenAI keys per organization. Keys only need to be added once.

Click API Keys in the top right of the Prompts page to open the Model Provider API Keys window:

Once added, you will have the option to select from the base models associated with each API key as you configure your prompts:

To remove the key, click API Keys in the upper right of the Prompts page. You’ll have the option to remove the key and add a new one.

Add OpenAI, Azure OpenAI, or a custom model

Use an OpenAI key

You can only have one OpenAI key per organization. For a list of the OpenAI models we support, see Features, requirements, and constraints.

If you don’t already have one, you can create an OpenAI account here.

You can find your OpenAI API key on the API key page.

Once added, all supported OpenAI models will appear in the base model options when you configure your prompt.

Use an Azure OpenAI key

Each Azure OpenAI key is tied to a specific deployment, and each deployment comprises a single OpenAI model. So if you want to use multiple models through Azure, you will need to create a deployment for each model and then add each key to Label Studio.

For a list of the Azure OpenAI models we support, see Features, requirements, and constraints.

To use Azure OpenAI, you must first create the Azure OpenAI resource and then a model deployment:

1. From the Azure portal, create an Azure OpenAI resource.

note

If you are restricting network access to your resource, you will need to add the following IP addresses when configuring network security:

• 3.219.3.197
• 34.237.73.3
• 44.216.17.242

2. From Azure OpenAI Studio, create a deployment. This is a base model endpoint.

When adding the key to Label Studio, you are asked for the following information:

Field	Description
Deployment	The is the name of the deployment. By default, this is the same as the model name, but you can customize it when you create the deployment. If they are different, you must use the deployment name and not the underlying model name.
Endpoint	This is the target URI provided by Azure.
API key	This is the key provided by Azure.

You can find all this information in the Details section of the deployment in Azure OpenAI Studio.

Use a custom LLM

You can use your own self-hosted and fine-tuned model as long as it meets the following criteria:

• Your server must provide JSON mode for the LLM.
• The server API must follow OpenAI format.

Examples of compatible LLMs include Ollama and sglang.

To add a custom model, enter the following:

• A name for the model.
• The endpoint URL for the model. For example, https://my.openai.endpoint.com/v1
• An API key to access the model. (Optional)
• An auth token to access the model. (Optional)

Create a Prompt

From the Prompts page, click Create Prompt in the upper right and then complete the following fields:

Example project types

Text classification

Text classification is the process of assigning predefined categories or labels to segments of text based on their content. This involves analyzing the text and determining which category or label best describes its subject, sentiment, or purpose. The goal is to organize and categorize textual data in a way that makes it easier to analyze, search, and utilize.

Text classification labeling tasks are fundamental in many applications, enabling efficient data organization, improving searchability, and providing valuable insights through data analysis. Some examples include:

• Spam Detection: Classifying emails as “spam” or “ham” (not spam).
• Sentiment Analysis: Categorizing user reviews as “positive,” “negative,” or “neutral.”
• Topic Categorization: Assigning articles to categories like “politics,” “sports,” “technology,” etc.
• Support Ticket Classification: Labeling customer support tickets based on the issue type, such as “billing,” “technical support,” or “account management.”
• Content Moderation: Identifying and labeling inappropriate content on social media platforms, such as “offensive language,” “hate speech,” or “harassment.”

Named entity recognition (NER)

A Named Entity Recognition (NER) labeling task involves identifying and classifying named entities within text. For example, people, organizations, locations, dates, and other proper nouns. The goal is to label these entities with predefined categories that make the text easier to analyze and understand. NER is commonly used in tasks like information extraction, text summarization, and content classification.

For example, in the sentence “Heidi Opossum goes grocery shopping at Aldi in Miami” the NER task would involve identifying “Aldi” as a place or organization, “Heidi Opossum” as a person (even though, to be precise, she is an iconic opossum), and “Miami” as a location. Once labeled, this structured data can be used for various purposes such as improving search functionality, organizing information, or training machine learning models for more complex natural language processing tasks.

NER labeling is crucial for industries such as finance, healthcare, and legal services, where accurate entity identification helps in extracting key information from large amounts of text, improving decision-making, and automating workflows.

Some examples include:

• News and Media Monitoring: Media organizations use NER to automatically tag and categorize entities such as people, organizations, and locations in news articles. This helps in organizing news content, enabling efficient search and retrieval, and generating summaries or reports.
• Intelligence and Risk Analysis: By extracting entities such as personal names, organizations, IP addresses, and financial transactions from suspicious activity reports or communications, organizations can better assess risks and detect fraud or criminal activity.
• Specialized Document Review: Once trained, NER can help extract industry-specific key entities for better document review, searching, and classification.
• Customer Feedback and Product Review: Extract named entities like product names, companies, or services from customer feedback or reviews. This allows businesses to categorize and analyze feedback based on specific products, people, or regions, helping them make data-driven improvements.

Text summarization

Text summarization involves condensing large amounts of information into concise, meaningful summaries.

Models can be trained or fine-tuned to recognize essential information within a document and generate summaries that retain the core ideas while omitting less critical details. This capability is especially valuable in today’s information-heavy landscape, where professionals across various fields are often overwhelmed by the sheer volume of text data.

Some examples include:

• Customer Support and Feedback Analysis: Companies receive vast volumes of customer support tickets, reviews, and feedback that are often repetitive or lengthy. Auto-labeling can help summarize these inputs, focusing on core issues or themes, such as “billing issues” or “technical support.”
• News Aggregation and Media Monitoring: News organizations and media monitoring platforms need to process and distribute news stories efficiently. Auto-labeling can summarize articles while tagging them with labels like “politics,” “economy,” or “health,” making it easier for users to find relevant stories.
• Document Summarization: Professionals often need to quickly understand the key points in lengthy contracts, research papers, and files.
• Educational Content Summarization: EEducators and e-learning platforms need to distill complex material into accessible summaries for students. Auto-labeling can summarize key topics and categorize them under labels like “concept,” “example,” or “important fact.”