Beyond accuracy: What quality really means in the gen AI era
Traditional AI measured quality in decimals. For tasks like image classification or named entity recognition, annotation quality could be assessed with near-mathematical precision. But generative and agentic AI models are different: they demand context, creativity, nuance, and human judgment.
That’s why Sigma has redefined data quality for this new generation of AI. Our approach ensures human-annotated data is not just technically correct, but contextually aligned, ethically grounded, emotionally intelligent, and culturally fluent.
We don’t just train machines to recognize patterns — we help them understand people.
Sigma’s ten standards of quality for gen AI
As explored in our white paper, Sigma defines quality in generative AI not only as factual correctness, but through ten key dimensions that reflect the complexity of human communication and intent:
- Cultural sensitivity: Annotation teams reflect diverse voices, accents, and regions — essential for AI to understand global audiences.
→ Read more: Linguistic diversity in AI and ML - Accuracy and inter-annotator agreement: Even with subjective tasks, we apply structured QA, such as IAA scoring and expert validation.
→ Read more: ¿Qué es la anotación de datos? - Contextual nuance: We train annotators to identify tone, sarcasm, emotional shifts, and implied meaning — tasks that even models struggle with.
→ Read more: Human annotators in AI - Domain expertise: We match annotators to specific verticals (e.g. healthcare, legal) and validate outputs with SME review panels.
→ Read more: Medical image annotation: goals, use cases & challenges - Judgment and prioritization: Our team scores annotation importance, flags uncertainty, and identifies content most likely to train safe, useful models.
- Summarization quality: Distilling key points from long-form inputs requires fluency, accuracy, and structure — not just compression.
→ Read more: How Generative AI is Transforming the Role of Human Data Annotation - Language logic and coherence: We review for fluency, flow, and natural phrasing, ensuring that LLM outputs “sound” human.
- Creative and open-ended thinking: AI needs to model more than facts — it must learn to tell stories, explore ideas, and imagine possibilities.
→ Read more: The future of AI is human - Dataset depth and diversity: We avoid overfitting by curating balanced datasets across domains, use cases, and edge scenarios.
→ Read more: Training data for machine learning: here’s how it works - Bias mitigation and ethical safeguards: Our processes integrate red teaming, fairness audits, and inclusive annotation practices.
→ Read more: Ethical AI vs. Responsible AI
A continuous loop of quality assurance
We don’t view quality as a one-time test. Our human-in-the-loop workflows emphasize:
- Preventative quality: Precise guidelines, domain-matched teams, and onboarding filters to reduce upfront risk.
- Real-time quality: Live accuracy monitoring, annotator feedback, and iterative reviews.
- Reactive quality: Escalation pathways, spot audits, retraining, and continual refinement of edge cases.
We believe that quality is co-created by humans and systems working together in feedback loops, not enforced top-down. Our project managers, clients, and annotators collaborate in real time to refine tasks, resolve ambiguity, and elevate consistency.
Why quality is Sigma’s differentiator
- Not crowdsourced. Every annotator is trained, tested, and matched to your project.
- Not static. Quality evolves as your model matures. We scale QA at every phase of development.
- Not optional. For generative AI to be safe, factual, and trustworthy, quality can’t be an afterthought. It must be engineered from the start.