{"id":5880,"date":"2026-09-26T14:47:17","date_gmt":"2026-09-26T12:47:17","guid":{"rendered":"http:\/\/progpret.fr\/?p=5880"},"modified":"2026-10-01T02:02:59","modified_gmt":"2026-10-01T00:02:59","slug":"the-risk-of-assuming-understanding","status":"publish","type":"post","link":"http:\/\/progpret.fr\/index.php\/2026\/09\/26\/the-risk-of-assuming-understanding\/","title":{"rendered":"The Risk Of Assuming Understanding"},"content":{"rendered":"

From chatbots giving incorrect advice to transcription tools with a taste for creative writing, these AI hallucinations examples prove the need for robust testing and AI observability. By learning from these errors, we can work towards creating more reliable and responsible AI systems that work as expected. You can use Retrieval-Augmented Generation (RAG) to ground LLM outputs in trusted data sources, such as company policies or documents. Though this is a composite example, LLMs are generally bad at math.<\/p>\n