Latest AI and tech news
Discover how Superhuman scaled human and LLM evaluation workflows with SuperAnnotate while improving flexibility, speed, and engineering efficiency.
Discover the importance of text annotation for managing unstructured data. Learn methods, types, and use cases in our comprehensive guide.
Check out our guide on semantic segmentation and its use cases to learn more about how to properly label specific regions of an image.
Explore Enterprise AI in 2026 – trends, infrastructure, and evaluation frameworks shaping the next phase of GenAI in large organizations.
Large language models (LLMs): A complete overview from their workings to future trends, challenges, LLM market leaders and more.
Automate your data-to-model pipeline with SuperAnnotate & Fireworks AI. Build quality datasets, fine-tune models, and deploy through fully connected workflows.
Explore why human-in-the-loop (HITL) feedback is essential for building real-world AI systems that perform, adapt, and improve over time.
Learn what Agentic AI is, how it works, its benefits and failures, and the best practices enterprises use to make AI agents more reliable.
Explore how ServiceNow leveraged SuperAnnotate to build StarFlow, a domain-specific vision-language model that now outperforms GPT-4o.
We’re excited to announce the launch of Agent Hub - a major new feature integrating data agents natively into SuperAnnotate.
How to write annotation style guides that align business needs with annotator tasks for reliable AI training data.
Explore how Flo Health, the leading women’s health platform, cut AI evaluation cycles by 10x to achieve clinical-grade accuracy.
See why many ML teams start in spreadsheets, when those spreadsheets break at scale, and what changes with a dedicated annotation platform.
Build or buy? Learn what enterprise AI teams should consider when choosing an annotation tool.
Explore how chain-of-thought (CoT( prompting enhances AI reasoning, offering a step-by-step guide to ensure more accurate and transparent LLM outcomes.
What is data labeling, and how does it work? Discover the different types of data labeling and learn the best practices to implement them effectively.
Discover how to build domain-specific LLMs with expert-labeled data, fine-tuning, and evaluation workflows to deploy high-accuracy AI in production.
Learn how to stay ahead in AI with stronger data pipelines, domain expertise, and adaptive training methods.
Learn what LLMOps is, how it differs from traditional MLOps, and how to reliably deploy, evaluate, and manage large language models in production.
Explore how SuperAnnotate and Fireworks AI accelerate GenAI. Meet us at AWS Summit NYC 2025 to learn about next-gen enterprise AI solutions.