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Gen AI Based Transformation with Smarter Data for a Global Tech Giant 

Gen AI

Discover how a global tech leader accelerated GenAI development with Qualitest’s high-quality data solutions, boosting agility, accuracy, and AI performance at scale.

Challenge

The client needed high-quality, labeled conversational data to train and refine its Gen AI systems. They wanted to improve response accuracy, support diverse use cases, and enhance the contextual understanding of their large language models.

Solution

Qualitest delivered a comprehensive set of data creation, validation, and analysis tasks to support Gen AI development. This included generating synthetic data, evaluating AI responses, fixing errors, and building custom datasets to train and fine-tune the models.

Results

High-quality data pipeline

Scalable, high-quality data pipeline with a faster response to evolving requirements.

Efficient Gen AI

Enabled consistent, efficient Gen AI product development with improved agility, accuracy, and model performance.

Client overview

The client is a global technology powerhouse known for pioneering advancements in internet-related services and products. Their portfolio spans online search, digital advertising, cloud computing, software, and consumer electronics. With innovation at the core of their business, the client continuously invests in cutting-edge technologies like Generative AI to boost user experience and maintain competitive advantage.

The client needed a partner with proven expertise in building high-quality and scalable data pipelines with the ability to quickly adapt to evolving GenAI requirements.

Business Needs and Objectives

The client wanted to accelerate the development and refinement of its Generative AI (Gen AI) systems. To do this, they needed high-quality, annotated data to train and improve the performance of their large language models (LLMs).

Specifically, they wanted to:

  • Generate conversational query data to feed their machine learning models.
  • Tag, rate, and categorize queries using proprietary testing tools.
  • Translate, tag, and contextualize data captured in worksheets.

The overarching goal was to build a scalable, accurate, and responsive AI that could support their suite of services and products.

Solution

Qualitest was engaged to support multiple Gen AI product development tasks critical to enhancing the performance and agility of the client’s LLMs. Our scope included a range of data operations, evaluations, and training initiatives: 

Data Creation & Curation

Created synthetic data to expand training coverage and supplemental content data to diversify model training. We also developed unique datasets to support training tool development and targeted datasets to fine-tune the LLM.

Evaluation & Quality Assurance

Compared and evaluated multiple AI-generated responses for quality benchmarking and rated response accuracy to assess AI output quality. Qualitest also reviewed and analyzed facts for accuracy and relevance to provided expert feedback on AI-generated content.

Data Enrichment & Contextualization

Identified and corrected errors in data and AI-generated responses. We contextualized content to improve model understanding and disambiguated user queries and model responses. Enabled topic-switching scenarios in conversational data.

Key Benefits

To support scalable and reliable Gen AI development, our approach to data operations was built on a foundation of structure, efficiency, and adaptability. The following key benefits highlight how our framework delivered consistent value across the data lifecycle:

Defined delivery process:

Enabled consistent production of high-quality data

High agility and faster response:

Well-structured workflows allowed rapid adaptation to evolving data instructions and new use cases.

Unified data creation:

Supported seamless Gen AI product development and training by providing reliable, structured data pipelines. 

End-to-end data lifecycle support:

Delivered comprehensive support across the GenAI data lifecycle, enabling seamless scaling and innovation.

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