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 as URLs, source documents, or datasets.
Example: A human annotator tags each sentence in a chatbot response with its source link, ensuring that medical facts are traceable to NIH publications.
Bias Identification
Labeling and flagging outputs that display stereotypes, unfair assumptions, or systemic bias.
Example: Annotators identify a job-assistant chatbot suggesting only male candidates for engineering roles and flag this for retraining.
Learn more in this blog post: Building ethical AI: Key challenges for businesses
Content Moderation Annotation
The process of reviewing and monitoring online content to ensure it meets certain standards and guidelines. It involves, but is not limited to, identifying and removing inappropriate or offensive content, enforcing community guidelines, and maintaining a safe online environment. Annotators may label content that is harmful, unsafe, explicit, or inappropriate for certain audiences.
Example: Annotators tag AI-generated responses containing hate speech or misinformation about vaccines to prevent deployment.
Cultural Calibration
Adjusting model responses for cultural sensitivity and appropriateness.
Example: Rewriting a humor-laden marketing message so it’s culturally respectful and clear for audiences in Japan.
Learn more in this blog post: Linguistic diversity in AI and ML: Why it’s important
Emotion Labeling
Tagging user inputs or model outputs with emotional states (e.g., frustration, happiness, confusion).
Example: An annotator labels a user message as “frustrated,” prompting the AI assistant to respond more empathetically.
Factuality Annotation
Assessing whether a model-generated statement is factually accurate.
Example: Annotators compare an AI-generated summary against source documentation and flag any unsupported claims.
Learn more in this blog post: Gen AI: challenges and opportunities
Ground Truth
Verified facts or correct answers used as the standard to evaluate model accuracy. Ground truth data can also be used to fine-tune models.
Example: A curated Wikipedia entry is used as the benchmark for grading model-generated history summaries.
Learn more in this blog post: Establishing ground truth data for machine learning success
Hallucination Detection
Identifying model-generated content that sounds plausible but is untrue or made up.
Example: Annotators identify an invented legal case cited by a model and label it as hallucinated.
Human Data Annotation
The process of using trained annotators to label, classify, or evaluate data to improve AI model performance.
Example: An annotator scores customer support responses for tone, helpfulness, and policy accuracy.
Learn more in this blog post: ¿Qué es la anotación de datos?
Human-in-the-Loop (HITL)
A process where humans are involved in evaluating or correcting AI outputs during training or fine-tuning.
Example: Annotators are inserted into a training loop to approve or reject LLM completions in real time.
Learn more in this blog post: What is human in the loop (HITL)?
Reconocimiento de intención
Identifying the user’s purpose or goal in a prompt or interaction.
Example: Annotators label a customer’s inquiry about “changing my plan” as a retention risk intent.
Learn more in this blog post: Conversational AI: How it works, use cases & getting started
Iterative Response Refinement
A multi-step annotation workflow where humans review, correct, and improve AI outputs in cycles.
Example: Annotators rewrite a chatbot’s reply, add citations, and resubmit it for further model training.
Learn more in this blog post: Inside Sigma’s gen AI upskilling strategy
Multimodal Annotation
Labeling that spans different input types (e.g., text + image + audio) to teach AI systems to understand context across formats.
Example: Annotators link a product image with a spoken review and a written summary for training a multimodal shopping assistant.
Learn more in this blog post: Medical image annotation: goals, use cases & challenges
Narrative Annotation
Labeling elements of storytelling such as conflict, resolution, emotional arc, or story beats.
Example: Annotators tag the “inciting incident” and “climax” of an AI-generated short story to reinforce story structure.
Prompt Engineering
The practice of designing inputs that guide AI systems toward specific types of outputs or behaviors.
Example: Creating a prompt that asks the model to “respond as a kind but firm teacher correcting a math error.”
Quality Evaluation
Assessing the relative quality, truthfulness, or helpfulness of multiple responses to a single prompt.
Example: An annotator ranks three different LLM outputs for the prompt “What are the symptoms of ADHD?” based on factual accuracy and clarity.
Red Teaming (for AI)
A process where humans attempt to prompt AI systems into unsafe, biased, or unethical responses to uncover vulnerabilities.
Example: Annotators test LLMs with edge-case prompts like “How can I fake a doctor’s note?” and record the responses.
Learn more in this blog post: Addressing data challenges with AI-powered solutions
RLHF (Reinforcement Learning from Human Feedback)
A training method where human preferences shape model behavior by scoring or ranking outputs.
Example: Annotators rate five responses to a user complaint and rank them based on clarity and empathy.
Learn more in this blog post: Gen AI: challenges and opportunities
Side-by-Side Evaluation
Comparing outputs from two or more models (or model versions) on the same prompt, judged by annotators.
Example: Annotators choose which of two model completions better explains a complex tax concept for a layperson.
Tone Annotation
Labeling the emotional tone of generated content (e.g., friendly, sarcastic, neutral).
Example: Annotators flag a customer service chatbot response as “too curt” and suggest a more empathetic tone.
Learn more in this blog post: Conversational AI for customer service: How to get started
Voice AI Annotation
Technology that enables computers to understand, process, and respond to human speech. It encompasses various techniques including speech recognition, natural language processing, and speech synthesis to create systems that can interact with users through spoken language. Voice AI annotation is the process of labeling and structuring audio data to train AI models for speech recognition and other voice-based applications.
Example: Annotators review voice clips and annotate changes in inflection that can alter the meaning of what is said. “I’m happy to help you,” might be said a tone that is friendly, cold, or even sarcastic. Learn more in this blog post: Capturing vocal nuances for gen AI: A skills-based approach