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Notes on multi-model AI
Plain writing on getting more out of AI by running several models at once: why it beats a single model, how the pieces fit together, and how to choose.
Why AI makes things up, and how to catch it
A made-up answer looks exactly like a real one: same tone, same confidence. Here is why models invent facts, and two checks that actually catch it.
June 17, 2026 · 4 minWhat is an LLM judge?
When several models answer and you get back one result, a judge ran in between. Here is what an LLM judge does, and why it is not the most expensive model in the run.
June 14, 2026 · 4 minWhy one AI model is not enough
A single model gives you one confident answer, right or wrong. Running several and combining them is how you catch what one would miss.
June 14, 2026 · 5 minFrom several answers to one: how synthesis works
Running several models is only half the job. The other half is combining their answers into one result you can actually use. Here is how that works.
June 14, 2026 · 5 minWhich AI model should you use? A quick field guide
Claude, GPT, Gemini, DeepSeek, Qwen and the rest each have a sweet spot. A plain guide to which model to reach for, and when not to choose at all.
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