LLM Fine Tuning
Improving User Experience Through Structured LLM Fine-Tuning
Challenge
A leading enterprise faced significant obstacles with their large language models LLMs). The models frequently produced hallucinations, biased outputs, and incomplete responses, making them unreliable for real-world deployment. Internally, the client’s team wanted to prioritize scaling and training their core LLMs rather than diverting resources to prompt design, dataset creation, and benchmarking. They needed a partner with both technical expertise and domain knowledge to reduce errors, enforce safety guardrails, and align outputs with their business context.
DDD’s Solution
Digital Divide Data (DDD) deployed its Human-in-the-Loop Fine-Tuning services to address these challenges. Our subject matter experts curated task-specific datasets, ensuring they were accurate, privacy-safe, and aligned with the client’s industry needs. Using Supervised Fine-Tuning (SFT), we adapted strong base models to perform with greater precision and compliance. Our team also implemented prompt engineering strategies and scenario-based benchmarking to validate model improvements at each
stage. Through red teaming and adversarial testing, we identified safety risks and biases, hardening the models against harmful outputs and ensuring consistent, context-aware responses
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Impact
We delivered a comprehensive set of prompts and responses crafted in the proper syntax and style for each domain, ensuring factual accuracy and alignment with the client’s context. These resources gave the client a structured way to verify, train, and tune their LLMs, streamlining the validation process and reducing the burden on internal teams. As a result, the models produced fewer hallucinations and biased outputs, while delivering more accurate, context-aware, and user-aligned responses. This translated into stronger model performance and a noticeably improved user experience