CrossChat
Long-form explainers on AI reliability, multi-model workflows, and the product mechanics behind CrossChat.
A doctor who's never seen your rare disease can still diagnose it from symptoms. They can identify a pattern beyond their direct experience. An interpolator would guess it statistically from similar known cases — and often get it wrong.
Read articleYou ask three colleagues for input before an important decision. You read multiple newspapers to get a balanced view. You request a second medical opinion. But when you query AI, you ask one model — and treat the output as fact.
Read articleYou ask the same question to GPT-4, Claude, and Gemini. GPT-4 answers A. Claude answers B. Gemini answers C. All three answers sound credible. Which is correct — or are all three wrong?
Read articleGPT-4 is more accurate than GPT-3. Claude Opus outperforms Claude Sonnet. Gemini Ultra achieves better results than Gemini Pro. Scaling works on average.
Read articleJanuary 2024. A research team didn't publish a new benchmark or a method that reduces hallucinations by another X%. They published a mathematical proof: LLMs as general-purpose solvers will always hallucinate — regardless of model size, training quality, or data volume.
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