The client is a leading U.S.-based artificial intelligence research organization dedicated to developing AI technologies that benefit humanity.

The client wanted to enhance its large language model (LLM) to align more closely with user expectations and address outputs on certain kinds of responses. The task was twofold: to rapidly and significantly scale the project team and to enhance the model's response accuracy and user satisfaction. This required a precise balance of rapid team expansion, adherence to evolving guidelines, and continuous improvement in data quality.

Faced with the challenge of enhancing model response accuracy and rapidly scaling the training team, the client and Turing executed on a meticulously planned two-pronged strategy:

• Rapid team expansion and onboarding: Designed a bespoke vetting process tailored to the client's unique requirements. In under two months, over 130 LLM trainers were sourced, vetted, and integrated into the project in record time. This process involved rigorous trial runs to ensure only the best candidates were onboarded. Dynamically adjusted resource allocation and promoted standout trainers to lead roles, ensuring a smooth scale-up that met urgent needs.
• Adaptable and precise training approach: Through a top-down communication strategy, Turing ensured every team member, from trainers to team leads and calibrators, remained fully aligned with frequent updates to task instructions. A dedicated team of professionals was established to maintain the highest data quality standards by providing continuous feedback and coaching, facilitating the rapid incorporation of insights and adjustments to improve task handling and data gathering.

The client's innovative and proactive approach, together with Turing, yielded remarkable outcomes, significantly enhancing LLM performance and user satisfaction.

• 130+ developers trained: Over 130 developers were hired and trained within a short time frame to complete more than 12,000 training tasks, boosting user satisfaction and accuracy of answers.
• Quality and satisfaction: Researchers expressed high satisfaction with the data quality and the expanded team’s remarkable performance, leading to significant improvements in model response quality.
• Ongoing success: The project’s success served as a benchmark for future initiatives, setting a new standard for operational excellence, scalability, and innovation in LLM development.