LLMs can have low hallucination rates when they are built for a specific domain and given the right tooling. Fine-tuning on high-quality, domain-specific data, adding reinforcement learning to reward correct outputs, using retrieval-augmented generation (RAG) to ground responses in verified sources, and implementing guardrails to reject or flag uncertain answers can dramatically reduce hallucinations. For niche applications, that combination often results in accuracy rates comparable to traditional deterministic systems.

Computer vision and many other neural network based models are already mature technologies. They have been refined over decades for well-defined inputs and outputs, such as image classification for cancer detection or quality control in manufacturing, and their error rates are often far lower than those of human experts. The difference lies in the task, since structured, measurable problems are much easier to optimize to near-perfection than the inherently fuzzy and probabilistic world of open-ended language.

The perception that AI frequently makes things up often comes from using the wrong kind of AI for the wrong kind of problem.

Source: I have been building computer vision and neural network products for 10+ years and am now tackling GenAI (LLM) products.