#aiaccuracy

Kirsten Poon is an artificial intelligence analyst from Edmonton with strong experience in building and improving AI systems. She works closely with data and technology teams to help AI deliver better and more reliable results for businesses. In this episode, Kirsten Poon explains 5 clear and practical tips to improve AI accuracy over time. It focuses on the importance of clean data, regular updates, steady performance checks, clear goals, and team collaboration. It helps understand how AI accuracy can change and how to manage it properly.

Human oversight enhances AI efficiency, ensuring accuracy, compliance, and actionable insights. At Statswork, we merge AI speed with human intelligence for smarter solutions.

Accurate AI starts with accurate data. In our latest blog, we break down how high-quality data annotation plays a critical role in training reliable AI models. From improving prediction accuracy to minimizing bias, well-labeled data is the backbone of every successful machine learning system. If your AI isn’t performing as expected, your data quality could be the missing link.

Text annotation is the key to accurate machine learning predictions. It’s like giving AI a map to understand language, not just recognize words. Without clear labels, models risk misinterpreting data, leading to poor outcomes and missed opportunities.

Want more relevant, precise AI outputs? Discover the techniques, examples, and best practices behind prompt engineering and start creating prompts that deliver results.