Getting the Most from Deep Research Models - by Alex Lawsen

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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

Last updated April 21 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…

Getting the Most from Deep Research Models - by Alex Lawsen

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