LLMs are redefining how organizations approach data labeling. From processing unstructured data to interpreting complex guidelines, Large Language Models are becoming a powerful support system for modern AI workflows.
In this carousel, explore:
✔ How LLMs compare with traditional annotation tools
✔ Why they are suitable for complex labeling tasks
✔ Key use cases across text, conversations, entities, and multimodal data
✔ Challenges you must consider before adopting LLM-driven annotation
✔ Why human–AI collaboration still delivers the best results
AI can speed up labeling—but quality comes from the right balance of automation and human expertise.