New AI-powered code and grammar critic improve quality and efficiency

To further improve the quality of labels and efficiency of raters, we are excited to announce the addition of code and grammar critics to the Labelbox platform. These AI-powered critics provide invaluable support to labelers and raters when writing or rewriting responses, facilitating the generation of high-quality training data at scale.

Elevating the standard of data for AI

AI-critics are specialized algorithms designed to analyze and assess the quality of code and text. They act as virtual assistants, meticulously examining responses for errors, inconsistencies, and areas for improvement. By providing immediate feedback and concrete suggestions, they enable users to quickly address potential issues, ultimately leading to higher quality training data. 

Key benefits of code and grammar critics include:

• Enhanced accuracy: These critics excel at identifying errors and inconsistencies in both code and grammar, ensuring that model outputs adhere to established standards and best practices.
• Improved data quality: By providing targeted feedback and suggestions, code and grammar critics help users generate high-quality training data that leads to improved model performance.
• Increased efficiency: The automated analysis and feedback provided by these critics streamline the evaluation process, allowing for faster iteration and refinement of AI models.
• Scalability: AI-powered critics enable efficient evaluation and improvement of large volumes of data, making them essential for training sophisticated AI systems.

Use Labelbox’s built-in critic for instant feedback

With the introduction of an AI-critic, Labelbox continues to push the boundaries of AI development, providing users with cutting-edge tools to create, evaluate, and refine their AI models with unprecedented precision and efficiency.

The built-in AI critic supports two key capabilities today:

• Grammar critic: For new responses written by a rater or labeler, the critic reviews spelling, grammar, and more to help deliver well-written, high-quality responses. 
• Code critic: For any code written, the critic performs a complete review to catch syntax errors, inconsistencies, and more across a wide range of common languages. 

A new “Get Suggestions” button now appears anytime you write and submit a user-defined response in a multimodal chat or LLM response evaluation project. With a single click, the AI critic will analyze both the grammar and code in your submitted response. 

After running, a list of suggestions are shown on the right hand side of the labeler response window. 

As you click through each suggestion, you are given three options:

• Preview: Display a new window with the current text highlighted on one side, and the new suggested text highlighted on the other side. You can choose to immediately apply that change or cancel. 

• Apply: Immediately update the response’s text and bypass the more in depth preview.  
• Discard: Do not change the original text and remove the suggestion. 

See the Labelbox AI critic in action

You can sign-in to experiment with the code critic yourself. But if you only have 2 minutes and want to see it in action, then click-through our AI critic interactive demo to see the new capability in action on both text and code. 

Improve your AI models with Labelbox

The introduction of code and grammar critics in Labelbox marks a significant step forward in streamlining and enhancing the AI development process. By providing real-time feedback and actionable insights, these tools empower users to generate high-quality training data with greater efficiency and precision. 

As AI models continue to grow in complexity and capability, the role of AI-powered critics will become increasingly vital in ensuring the accuracy, reliability, and overall quality of AI systems.

Want to learn more? Discover why Lablebox is the data factory for building frontier and task-specific models. 

And reach out to us anytime on our contact us page with any questions. We’re always happy to talk about data and quality!