Sure. By multi-model, I mean using multiple independent LLMs to verify each other instead of trusting one model blindly. Example: Ask GPT, Claude, and Gemini the same question and compare their outputs. What matters most is disagreement. If all models agree → higher confidence If they disagree → possible hallucination, hidden edge case, or weak reasoning Different model families fail differently, so disagreement becomes a useful signal for human review. I think future AI systems will rely more on model consensus and verification, not just a single “smart” model.

Solid breakdown, especially the part about not training from scratch. One concept I'd add to any AI engineer's mental model in 2026: model disagreement as signal. Most curricula teach you to pick "the best model" for a task. But once you're shipping AI features to real users, the harder skill is detecting when your single model is confidently wrong. I've started running the same prompt through 2-3 different model families (different training lineages, not different sizes of the same family) and using their disagreement as a quality signal. When they all agree, that's normal. When they split, that's where engineering judgment pays off. Curious if you've integrated multi-model verification into your own AI engineer learning path, or if it's still mostly single-model fluency in 2026 curricula?