Most people ask a local model to do too much at once: understand the idea, clarify the requirements, design the architecture, write the code, test the work, fix the bugs, and decide what comes next. Then, when the result falls apart, they blame the model.

“WE ArE RoBOTs. We NeED tO CoNfIRm YoU Are HuMaN. ArE YoU HUMaN? PLeAsE ConFiRm YoU ArE A HuMan.”

Large Language Models generate responses by analyzing patterns derived from vast datasets. However, they lack genuine comprehension or grounding in reality.