Why better data builds better AI
The role of data in teaching nuanced AI Generative AI doesn’t just need labeled data; it needs representative data. That means multilingual, multi-domain corpora designed to teach tone, sentiment, and context — not just keywords. Sigma’s multilingual, multitask corpus spans over 300,000 human-reviewed texts across 10 languages and seven NLP tasks, from sentiment analysis to […]
Teaching AI to truly understand what we mean
Why meaning matters for AI LLMs trained only on raw text often produce plausible but incorrect interpretations. The result: outputs that sound convincing but fail to reflect reality. When AI misses tone, emphasis, or structure, it can frustrate users, or worse, cause harm. Imagine a voice assistant that fails to distinguish between a polite suggestion […]
Why red-teaming your AI protects your brand and your users
Why traditional testing isn’t enough Most organizations validate AI systems with internal QA or benchmark datasets, but these don’t simulate adversarial conditions. Real users (or bad actors) may try prompts that testers never imagined — seeking confidential data, bypassing safety filters, or eliciting unethical instructions. Recent headlines show what happens when these safeguards aren’t in […]
Teaching AI to hear what we mean, not just what we say
When accuracy isn’t enough When a customer hears, “I’m happy to help,” they instantly know if the speaker truly means it — by tone, pacing, and emphasis. AI, however, often misses those cues. Large language models (LLMs) and voice systems may produce technically correct responses that land as emotionally tone-deaf, culturally inappropriate, or misaligned with […]
When accuracy isn’t enough: building truth into generative AI
Why generative AI creates new quality challenges Traditional AI trained on structured data often produced outputs that were binary: right or wrong. In generative AI, the boundaries blur. An LLM might summarize a document but omit a key fact, misattribute a quote, or confidently reference a study that doesn’t exist. Real-world incidents highlight the stakes: […]
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 […]
How red teaming AI reveals gaps in global model safety
Red teaming goes global Red teaming — intentionally probing AI models for weaknesses — has long been a key practice in AI safety. But most efforts focus on English, text-based interactions. Sigma AI decided to take things further. In our latest study, they pushed top models to their limits, examining how they behave in different […]
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 […]