LLM Fine Tuning
Optimizing Model Performance Through LLM Fine-Tuning Expertise
Challenge
A client working with large language models (LLMs) faced critical limitations in accuracy and trustworthiness. Their models often produced irrelevant, biased, or fabricated outputs, creating barriers to scaling into production. They needed a partner who could deliver domain-specific, structured training resources that would directly improve model quality and reduce risks.
DDD’s Solution
Digital Divide Data (DDD) applied its human-in-the-loop fine-tuning methodology to address these gaps. Our subject matter experts designed domain-aware prompts across the client’s areas of focus and paired them with fact-checked, context-rich responses. These were carefully structured into multiple task categories, including summarization, extraction, closed-form Q&A, ensuring the LLM could be tuned to handle both simple and complex workflows. In addition, DDD provided a framework for systematic validation and benchmarking, giving the client a clear process to measure improvements over time.
Impact
With these curated datasets and structured benchmarks, the client achieved more reliable, safer, and context-aware outputs from their LLMs. Hallucinations and biased responses were significantly reduced, while overall alignment with user intent improved. The combination of improved model performance and operational efficiency translated into a stronger user experience and accelerated path to eployment.