Why human skills are the secret ingredient in generative AI
Rethinking AI development — from code to human intelligence When most people think of artificial intelligence, they imagine complex algorithms and machine logic. But Sigma is proving that the most powerful AI systems begin with people. The company specializes in training individuals to perform generative AI data annotation — the behind-the-scenes work that fuels model […]
When “uh… so, yeah” means something: teaching AI the messy parts of human talk
A quick primer: what’s what (and why it matters) Signals, not noise: disfluency carries meaning A sentence like, “I — I can probably help … later?” encodes hesitation, caution, and weak commitment. If ASR or cleanup filters strip stutters, filler, or rising intonation, downstream models may over-state confidence. Annotation pattern Example “That’s a whole — […]
FAQs: Human data annotation for generative and agentic AI
What is human data annotation in generative AI? Human data annotation is the process of labeling AI training data with meaning, tone, intent, or accuracy checks, using expert human reviewers. In generative AI, this helps models learn to produce outputs that are truthful, emotionally appropriate, localized to be culturally relevant, and aligned with user intent. […]
Generative AI glossary for human data annotation
Agent evaluation The process of assessing how well an AI agent performs its tasks, focusing on its effectiveness, efficiency, reliability, and ethical considerations. Example: An annotator reviews a human-agent AI interaction, determining whether the person’s needs were met, and whether there was any frustration or difficulty. Attribution annotation Labeling where facts or statements originated, such […]
Enterprise AI software: Use cases from top tech companies
Gen AI is the new baseline for enterprise software Top-tier tech companies such as Microsoft, Salesforce, and Google are setting a new standard for AI enterprise software. Gen AI capabilities are becoming a must-have. Gartner projects that over 80% of software providers will embed gen AI into their products by 2026, driven by a demand […]
Why gen AI quality requires rethinking human annotation standards
From accuracy to agreement: A new lens on quality Traditional AI annotation tasks (e.g. labeling a cat in an image) tend to yield high human agreement and low error rates. Annotators working with clear guidelines often achieve over 98% accuracy — sometimes even 99.99% — especially when backed by tech-assisted workflows. But these standards don’t […]
Precision in data annotation: What’s needed for gen AI models
Precision in gen AI data annotation Gen AI models learn to create novel content. However, for these models to be reliable and useful, their content should be grounded in accurate information and logical structures. In gen AI data annotation, precision extends beyond accurate facts; it also encompasses creativity and nuance. Precise outputs should be factually […]
Human touch in gen AI: Training models to capture nuance
Humanity in gen AI data annotation Data annotation is not just about accuracy and precision. It requires human expertise and careful oversight to ensure AI models interact with the world in a meaningful, relevant, and responsible way. Drawing from our most recent whitepaper, “Beyond accuracy: The new standards for quality in human data annotation for […]
Accelerating the new AI: Key insights from our latest whitepaper
The key to gen AI success? High-quality data powered by human expertise Building accurate and reliable generative AI models demands vast amounts of high-quality training data. Achieving this is easier said than done: it requires the right blend of efficient workflows, deep-domain knowledge, and human oversight. With over 30 years at the forefront of AI […]