Client Profile.
The client is a leading AI training data solutions provider headquartered in San Francisco, USA, specializing in end-to-end ML lifecycle management. They partner with the world’s leading ML teams to accelerate the development of AI models.
Business Need.
The client manages a globally distributed workforce of over 100,000 freelancers across multiple time zones, which presents serious operational challenges. As the workforce grew, inconsistent annotation accuracy, high freelancer turnover, fraudulent activity, high training costs, regulatory complexities and other issues multiplied. This led to bottlenecks in productivity, accuracy, and cost-effectiveness.
To optimize resource management, maintain quality control, scalability, consistency, and improve operational efficiency, the client partnered with HitechDigital to:
- Develop high-quality prompts based on images for AI model guidance (Image & Text-to-Text (ITT) that guided AI models in producing responses that adhered to strict quality, relevance, and factual accuracy standards
- Implement a structured process including prompt creation, prompt approval, AI response generation, and final response evaluation before submission
- Replace their freelancer-based operations model with a more reliable, scalable solution
Project Challenges.
The project management team encountered several challenges as they developed a strategic, scalable, and high-quality workforce solution for successful project implementation. These included:
- Rapidly transitioning from the client’s existing freelancer-based operations that had become unsustainable due to increasing complexity and scale
- Deploying a dedicated, long-term team of prompt engineers to reduce dependency on freelancers while maintaining service continuity
- Implementing a structured training program to equip newly hired prompt engineers with necessary expertise
Solution.
HitechDigital deployed a strategic workforce solution which involved building a dedicated, highly trained team of prompt engineers, ensuring long-term reliability and adaptability. The solution aimed to enhance AI model performance through consistent, high-quality data annotation.
- A specialized, dedicated prompt engineering team was deployed, ensuring consistent, quality data annotation, crucial for enhanced AI model performance.
- Comprehensive training programs were implemented, focusing on project-specific workflows, advanced prompt engineering techniques, and strict adherence to quality assurance and compliance standards.
- This structured approach enabled rapid adaptation to evolving project demands, ensuring a scalable and efficient solution that met client’s high-quality standards.
Approach.
We deployed a structured workflow, prioritizing training, evaluation, and continuous feedback to ensure delivery of high-quality AI prompts. The approach was designed considering efficient project execution and enhanced AI model performance.
- Hiring and Training:
- A team of 20 prompt engineers was recruited with plans to scale to 70 in two months and trained in analytical skills and English communication.
- Emphasis was placed on comprehension, listening, and attention to detail, crucial for effective prompt creation.
- Training addressed prompt development, outcome analysis, and collaboration with technical teams.
- A/B testing and iterative improvement were taught, enabling data-driven prompt optimization.
- Certification and Testing:
- Following training, all candidates were required to pass an official project exam demonstrating their understanding of project requirements, quality expectations, and technical precision needed
- Less than 10% of total candidates shortlisted on the Outlier platform met the required competency levels
- Certification validated their ability to adhere to prompt approval criteria. It also confirmed their understanding of guidelines to generate prompts in nine different categories
- This ensured only qualified individuals worked on the project, maintaining a high standard of output
- This guaranteed that all team members understood the required quality expectations.
- Prompt Generation:
- Image-dependent prompts were created, adhering to competency guidelines and approval criteria
- AI-generated responses were evaluated for quality and compliance, ensuring alignment with client expectations.
- Reviewer ratings and feedback were communicated instantly to refine and optimize prompt effectiveness.
- Prompt generation, based on the inputs provided by the client, followed a structured process to ensure the desired output.
- Quality Check:
- Regular quality review sessions were conducted to ensure consistency and adherence to guidelines defined by the client.
- Ongoing client communication was maintained to align project objectives and address evolving requirements.
- Continuous performance monitoring ensured that our prompt engineers met high-quality benchmarks across the project.
- The quality check ensured that the prompts delivered were of the highest quality and consistent results.
- Iterative Feedback Mechanism:
- AI-generated responses were evaluated, and feedback was used to enhance model performance.
- The feedback loop allowed for refinements through structured feedback leading to improved model outputs
- This mechanism ensured prompt generation remained aligned with user expectations and enabled continuous improvement.
- Final Deliverables/Reports/Dashboards:
- Approved, high-quality prompts were delivered, meeting all competency and image-dependent criteria.
- Reviewed and rated AI prompts were sent to clients, ensuring alignment with guidelines and user expectations.
- Quality assurance reports, documented performance insights, and recommendations for improvement were regularly shared with the client.
Business Impact.
Significant reduction in operational costs without compromising quality.
Faster Turnaround Time (TAT) & increased volume handling
Higher annotation quality and accuracy
