Hurix Digital Builds Instruction-Focused Dataset for Enterprise-Grade LLM Training

Enterprises adopting LLMs need more than generic training data. They require domain-specific, instruction-focused datasets that are accurate, safe, and explainable. Without them, models risk producing vague, hallucinated, or even unsafe outputs.

Our client, a leading global AI research organization, tackled this challenge directly. They required over 30,000 instruction-response pairs that accurately represented real-world enterprise scenarios in finance, HR, and compliance-intensive fields. The dataset needed to be fact-based, consistent, and aligned with the HHH (Helpful, Honest, Harmless) framework, while also ensuring a clear audit trail for any revisions.

Existing open-source datasets fell short. They lacked domain specificity, produced overly generic outputs, and offered no transparency on how pairs were created. The result was poor enterprise performance and high rejection rates.

Hurix Digital created a custom dataset framework, developing a blueprint for instruction-response design covering tone, length, depth, and citations for each enterprise context. 

To reduce hallucinations, we trained our annotators to identify weak or unverified outputs, emphasizing source-based grounding and factual accuracy. A transparent feedback loop ensured continuous improvement across the team.

The outcome?

• 30,000+ enterprise-grade pairs, delivered on time
• 40% reduction in hallucination-related rejections
• Consistent tone and safety compliance across all responses

Download the EXCLUSIVE case study to explore how we created a zero-hallucination dataset pipeline for enterprise-grade AI training!