What sets successful Gen AI models apart? It’s not just the algorithm — it’s the human context behind the data. This whitepaper reveals how Sigma, a global leader in data annotation, delivers high-quality, scalable human-in-the-loop services that help generative AI systems reason, empathize, and perform better in the real world.

Packed with third-party research, operational benchmarks, and original findings from Sigma’s client work (including 4 of the top 5 global tech companies), this whitepaper explores how customization, feedback loops, and deep linguistic and domain expertise unlock real-world accuracy and scale.

What you’ll find:

• Sigma’s 5-factor model for defining training data quality
• How customized workflows improve both annotation speed and quality
• Why real-time feedback loops are critical to reducing hallucinations
• Research-backed insights into annotation performance and outcomes
• Case studies from high-complexity, multilingual, and high-risk domains
• Practical tactics to scale annotation without sacrificing data integrity

As generative AI moves from lab to production, the quality of your data becomes a competitive edge.

This whitepaper shows how to get it right — at scale.