Atla is revolutionizing the way developers build and maintain reliable Generative AI applications. With its advanced AI evaluation models, Atla helps you automate data labeling, optimize LLM outputs based on user preferences, and filter out unreliable results before they reach your users. Whether you're a startup or a global enterprise, Atla's offline and online evaluation tools ensure continuous improvement and confidence in your AI app's performance. Integrate seamlessly with CI pipelines to understand the impact of changes before deployment, and monitor your app in real-time to address any issues promptly. Start shipping more reliable GenAI apps faster with Atla's easy-to-use tools and get started for free today.

Automate Data Labeling

Atla's Automate Data Labeling feature is designed to significantly reduce the manual effort and costs associated with data annotation in generative AI applications. By leveraging AI scores and feedback, this function automates the data labeling process, making it more efficient and cost-effective. This not only speeds up the development cycle but also ensures that the data used to train models is of higher quality, which in turn leads to more accurate and reliable model outputs. This feature is closely related to the Optimize with Clear Targets function, as better labeled data can lead to more accurate model outputs, enabling continuous improvement based on user preferences.

Optimize with Clear Targets

The Optimize with Clear Targets feature allows developers to measure the quality of their Large Language Model (LLM) outputs based on user preferences, creating a virtuous cycle of continuous improvement. By setting clear targets and continuously evaluating the model's performance, developers can ensure that their LLM outputs are aligned with user expectations. This feature creates value by enhancing the user experience and trust in the AI application. It is closely related to the Automate Data Labeling function, as better labeled data can lead to more accurate model outputs, enabling this continuous improvement process.

Filter Out the Worst Outputs

Atla's Filter Out the Worst Outputs feature is crucial for maintaining the reliability and quality of generative AI applications. This function uses advanced models to identify and eliminate the worst outputs of the LLM app before they reach the users. By doing so, it significantly improves the user experience and builds trust in the AI application. This feature is related to both the Offline Evaluation and Online Evaluation functions, as these evaluations help in identifying the worst outputs, ensuring that only high-quality outputs are delivered to the users.

Offline Evaluation

The Offline Evaluation feature allows developers to test their prompts and model versions with AI evaluators before they are deployed to production. By automatically scoring performance and providing feedback on model outputs, this function helps developers understand the impact of changes on their app before they go live. This creates value by enabling more confident and informed decision-making during the development process. It is closely related to the Integrate with CI function, as both aim to ensure the reliability of the app before deployment.

Integrate with CI

Atla's Integrate with CI feature is designed to help developers understand how changes impact their app before they hit production. By integrating with Continuous Integration (CI) systems, this function enables faster and more confident shipping of updates. It creates value by reducing the risk of deploying faulty updates and ensuring that the app remains reliable and functional. This feature is closely related to the Offline Evaluation function, as both aim to ensure the reliability of the app before deployment.

Online Evaluation

The Online Evaluation feature allows developers to monitor their generative AI application in production to spot problems or drift. By continuously evaluating the app's performance, this function enables ongoing iterations and improvements, ensuring that the app remains reliable and of high quality. This creates value by maintaining the user experience and trust in the AI application. It is closely related to the Filter Out the Worst Outputs function, as both aim to improve the reliability and quality of the app in production.