Appen’s Latest Solutions Ensure Ethical and High-Performance AI Chatbots 

AI data supplier Appen has unveiled two novel products - AI Chat Feedback and Benchmarking - to help customers deploy large language models (LLMs) that provide accurate and unbiased responses, enhancing the quality and ethical nature of AI-generated content.

“Appen’s new evaluation products provide our customers with an essential trust layer that ensures they are releasing AI tools that are truly helpful and not harmful to the public. This trust layer is backed by robust datasets and processes that have proven effective in our 27 years of AI training work, and a team of over a million human experts who are attending to the nuances of the data,” said Armughan Ahmad, CEO, Appen.

AI Chat Feedback 

This tool helps subject matter experts evaluate and refine multi-turn live conversations. It allows them to assess, rate, and revise each response within the conversation, ensuring that the AI-generated content meets the desired standards.

Appen's AI Chat Feedback streamlines the management of data throughout multiple rounds of evaluation, providing customers with the necessary insights to enhance their models effectively.


Benchmarking is designed to assist customers in assessing model performance from various angles, including accuracy and toxicity. It provides a comprehensive evaluation of the model's performance in different aspects, helping users make informed decisions about its use.

Appen's Benchmarking tool addresses a crucial challenge faced by businesses aiming to swiftly enter the AI market: determining the most suitable large language model (LLM) for a specific enterprise application. The choice of model carries strategic implications for various aspects of an application, encompassing user experience, ease of maintenance, and profitability.

Customers gain the ability to assess the performance of different models based on commonly used or completely customized criteria. This tool, combined with Appen's pool of AI Training Specialists, allows for performance evaluations across demographic factors like gender, ethnicity, and language, which can be of particular interest.

Moreover, Benchmarking offers a flexible dashboard that streamlines the comparison of multiple models across various criteria, facilitating efficient decision-making in model selection for enterprise applications.