How AI Inference Works and Why It Makes LLM Outputs Unpredictable

AI language models generate outputs through a process called inference, where the model predicts the most statistically likely response based on input context rather than truly understanding or reasoning through a request. These probabilities are shaped during training and can be further refined through fine-tuning on narrower datasets. Because outputs are probabilistic and non-deterministic, the same question can yield different answers each time, making errors in agentic workflows common. Excessive or poorly managed context can degrade output quality, and overly rigid rules can inadvertently suppress creativity in open-ended tasks. To improve reliability, the author recommends standardising prompt formats and designing task-specific prompt schemas tailored to different objectives.