5 examples of successful AI tasks built on Prolific

Just as every modern product is becoming an AI one, building reliable AI applications requires human insight just as much as any technical metrics. AI teams face challenges that only diverse human perspectives can address, from testing model behavior in real-world scenarios to evaluating user impact. 

At Prolific, we bring together a global network of skilled participants who use our features to provide the human intelligence layer essential for creating reliable, trustworthy AI. Here are five examples of how teams can leverage Prolific to enhance their AI products with real-world feedback.

Who uses Prolific for AI development?

Prolific serves the full spectrum of AI development, from major research labs working on foundation models to startups building specialized applications. Leading AI research institutions use us to gather human feedback for model alignment and evaluation, while application developers tap into our network for targeted use cases like chatbot training and content validation.

Whether you're building customer-facing chatbots that get people the information they need sooner, fine-tuning language models that adapt to specific industry terminology or user preferences, or developing content generation systems for producing accurate, engaging materials at scale, Prolific helps you create better AI products faster. 

And with over 200,000 participants bringing different skills, interests, and life experiences, you'll find the right people for your specific needs.

5 ways AI developers use Prolific 

From fact-checking AI-generated content to training models to handle diverse documents, here are five ways AI developers can use Prolific to gather high-quality human input, refine their models, and deliver more effective AI applications.

1) Fact-checking AI-generated content

Content creation is one of the top use cases for generative AI applications, as highlighted in a McKinsey report showcasing the growing adoption of AI tools for generating diverse forms of written and multimedia content. Organizations using AI to help generate content need to validate that the output remains accurate and faithful to the source material. 

Using AI Task Builder, Prolific’s data annotation product, it’s easy to set this up by uploading pairs of human-written and AI-written articles as CSV datasets. They can configure tasks for taskers to evaluate each pair, checking aspects like:

• Factual accuracy comparing the AI-written to the original content
• Proper handling of direct quotes and key dates
• Preservation of original context

Recruiting taskers who are fluent in English and have the right cultural context or local knowledge means teams can get high-quality human evaluation of their AI content at scale. As a result, organizations understand how well their AI content generation is performing and where it needs improvement.

The process is quick and efficient. Taskers can evaluate large volumes of content in hours, providing the feedback teams need to make sure their AI-generated content meets quality standards.

2) Evaluate AI-translated content

AI task builder can help companies validate whether an AI model acting as a judge can accurately assess translations produced by another AI model. Here's how it works.

Teams can upload datasets containing English source text, Japanese translations generated by one AI model, and quality scores assigned by a second AI model. Using AI Task Builder, they can configure tasks and recruit bilingual participants from Prolific’s 200,000+ participants speaking more than 80 languages to review each set of inputs and validate if the scores accurately reflect translation quality.

Through Prolific's API, teams can create a continuous annotation process, streaming tasks to participants who are fluent in both English and Japanese. The approach allows for ongoing validation and improvement of the AI judge, helping ensure reliable translation quality assessment that can be integrated into real-time document workflows.

3) Fine-tuning domain-specific models

Legal tech developers building AI assistants face the challenge of making sure their chatbots truly understand complex legal terminology and regulations. A legal assistant chatbot needs to interpret queries correctly and provide accurate, compliant advice without ambiguity.

By uploading legal documents and sample chatbot responses for review, development teams can have legal experts evaluate the AI's understanding and output. Tasks can be configured to check multiple aspects of the responses: Are legal terms interpreted correctly? Do the answers follow regulatory guidelines? Is the chatbot giving clear, unambiguous advice?

Using Prolific's filters, teams can recruit annotators with legal backgrounds to provide expert feedback on accuracy and compliance. It’s a targeted approach that helps developers fine-tune their models while reducing time-to-market and improving how their chatbots handle complex legal queries.

4) Teaching AI to understand customer emotions

Support teams handling thousands of customer messages need smart ways to identify priority cases and gauge customer satisfaction. Training AI to understand the subtleties of human emotion isn't simple, especially when dealing with industry-specific language and context.

Quality training data forms the foundation of reliable sentiment analysis. Teams can build effective models by having annotators:

• Classify the sentiment of customer reviews, social media posts, and tickets
• Add context and reasoning for cases with mixed or unclear emotions
• Flag urgent issues that need immediate attention

Working with annotators who understand specific sectors like e-commerce or hospitality will help AI systems learn the unique language customers use in different contexts. Support teams gain the ability to automatically catch urgent cases, track customer satisfaction trends, and respond more effectively to keep customers happy.

5) Training AI to handle diverse documents

Teams handling building permits and construction data can use AI to make sense of messy, inconsistent documents. With each city and county using different formats and terminology, obtaining accurate training data isn't easy, especially at scale.

By creating focused labeling tasks, it’s possible to tap into expertise from people who know the industry. Participants help sort permits into clear categories and pull out the important details, turning varied documents into structured data that AI can learn from.

Working with participants who understand construction means the training data captures how permits really work in the field. Development teams can quickly build reliable datasets that help their AI handle real-world documents better, making it easier to spot construction trends and match projects with the right contractors.

Get started with Prolific

Building better AI requires human input at every stage, from training to testing to validation. Prolific's features make it easy to connect with the right participants and gather the data you need. Ready to improve your AI? Sign up for early access and start working with our global pool of taskers.