Understand GenAI Use Cases by Understanding Limits

The easiest way to get generative AI use cases wrong is to start with excitement and work backwards. The more useful starting point is the opposite: understand where the technology breaks, and then look for places where those limits are acceptable or can be managed.

That is the frame of this article. Creative generation can live with surprise. Enterprise workflows usually cannot, especially when hallucinations can influence decisions, customer communication, or operational processes.

Prompt engineering is treated as the practical first step, but not as magic. Longer prompts add cost and complexity. Fine-tuning can help in specific cases, but it brings its own trade-offs around data, skills, training effort, and model quality.

The important question is not “where can we use GenAI?” It is where incorrect output is tolerable, where it is dangerous, and what mitigation is realistic.