Gen AI glossary for LLMO
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 inter‑annotator agreement is critical to best‑in‑class gen AI training
What is inter‑annotator agreement (IAA) and why is it important? IAA measures how consistently multiple annotators label the same content. It helps quantify whether annotation guidelines are clear and whether annotators share a reliable understanding. Common metrics: Even seasoned experts often show α = 0.12–0.43 in high‑subjectivity tasks like emotional attribute scoring, especially before refining […]
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 […]
What Humans Hear That Machines Miss: 10 Hidden Language Cues
Irony and Sarcasm What it is: Saying the opposite of what is meant, often with a tonal cue. Example: “Oh, fantastic job…” said with clear frustration. Why machines miss it: Literal interpretation of words leads to mislabeling intent. Pragmatic Implicature What it is: Inferring meaning beyond explicit words, based on context. Example: “It’s cold in […]
Preventing AI bias: How to ensure fairness in data annotation
What is bias in AI? AI bias occurs when an AI model generates results that systematically replicate erroneous and unfair assumptions, which are picked up by the algorithm during the machine learning process. For example, if an AI system designed to diagnose skin cancer from images is primarily trained with images of patients with fair […]
Golden datasets: Evaluating fine-tuned large language models
What is a golden dataset? A golden dataset is a curated collection of human-labeled data that serves as a benchmark for evaluating the performance of AI and ML models, particularly fine-tuned large language models. Because they are considered ground truth — the north star for correct answers — golden datasets must contain high-quality data that […]
Best practices to scale human data annotation for large datasets
The data dilemma: How much training data is enough for LLMs? Among the many challenges of training LLMs is the demand for gigantic amounts of training data. The exact volume varies based on the model’s intended use case and the complexities of the language domain. To determine the optimal dataset size, experts recommend experimenting with […]
How do you know it’s time to outsource data annotation?
You need to move quickly but without compromising quality. In-house annotation? For many organizations, it isn’t sustainable anymore. But how do you know it’s time to outsource data annotation? If you’re struggling to keep pace with your data annotation demands, facing a bottleneck, or simply want to optimize your AI development pipeline, read on to […]