Getting the Most from Deep Research Models - by Alex Lawsen
I'm excited about the potential of Deep Research (DR) models. As with many things, it seems like the quality of output you get depends a lot on how well you construct your prompt. I’ve seen lots of people be very unimpressed with the output, often for reasons like “the models are too credulous of low-quality sources”. But that seems like the sort of thing it should be pretty easy to fix. If I had an intern or a research assistant whose reports were over-indexing on whatever clickbait they’d found first, I’d give them a quick rundown of how to determine source quality and then tell them to try again. Except, actually, this is exactly the sort of information I would expect a language model to know. Guides for evaluating sources are all over the internet. I’d tell my intern/RA to get some guidance from Claude or Gemini 2.5 on this, rather than explain it myself. The guidance might end up looking like this. After a fair bit of experimentation, I've built a Claude project that handles all
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