My deep research use cases

Deep research is underappreciated. The feature exists in all three LLM chat apps I pay for (ChatGPT, Claude and Gemini) and is unfortunately heavily rate limited, which makes sense given the likely token cost. But the massive amount of tokens burnt on reading and analyzing hundreds of articles is the reason it can have such a big impact on some tasks.

Here are some examples of queries where I've found deep research valuable.

Buying clothes

I like getting high quality apparel at a reasonable price. I hate searching hours for it:

Find me pants that are office suitable, work with a dress shirt, but are made of some breathable and modern material. Could be called travel pants. Find what is generally recommended to look good and decent value. Consider reddit male fashion advice and similar sources, and also think of options you know and search more broadly. Take manufacturer/retailer claims with a grain of salt, put more weight onto 3rd party / user reviews. Up to 200€ but less is better. Can consider different style. Color something pretty neutral. Machine washable. Ships to EU (Estonia specifically)

(Here and below, the quoted text is my prompt to the LLM app with deep research enabled.)

I reviewed the 7 results and ended up buying the top 1 recommendation, spending a total of about 5 minutes of my own time.

The key part for me here was "put more weight onto 3rd party / user reviews" including recommending r/malefashionadvice as a specific useful source. Without this, the LLM tends to take marketing statements at face value, making it much less useful at distinguishing between good product vs good marketing.

Buying software

I often feel like "there must exist many products that solve this problem". For example, for product roadmapping:

find me best in class roadmapping tools. Rely not on product marketing materials or SEO pages, but user blog posts such as lenny newsletter. Include workflows which use simple software

Or open-source tools for calculating metrics off of Github repos:

find me open source tools that give metrics on top of github repos (all repos in org, not just one)

Even if I don't find anything I end up buying, it is valuable to know no easy option exists.

Buying last-minute tickets

A genuine problem a friend had, and the solutions from deep researched allowed him to get the tickets:

How can I get Louvre tickets couple days ahead, if all has been sold out? 2 tickets, specifically for this Friday (today is Tue).

Understanding state of the art - legal analysis

This is obviously something I don't take at face value, but I wanted to know the (published) state of the art thinking on how copyrightable AI-generated code is:

Is there analysis or case law or analogies about how much human contribution is needed in a code contribution, vs AI contribution, for it to be copyrightable, in the US

I got roughly the same answer I'd arrived at previously: the lines are still pretty unclear.

Understanding state of the art - psychology

I'd heard that most people assume genetics plays a much smaller role in life outcomes than it really does. The key question I had, as a parent, was: in which places does nurture actually matter?

Which parts of childraising are most strongly affected by parental behavior as opposed to genetics? All ages and areas. Generalizable findings

Understanding how others solve a problem

Even if nobody out there has exactly your situation, looking to others for inspiration can still be a useful exercise. Like what are useful product metrics for AI applications:

I want to know what product usage and value metrics are used by the leading agent/AI companies and products. Especially in enterprise context, not b2c * chatgpt * cursor * github copilot * a few others if you know some

Or the concept of forward-deployed engineering

Give me report on palantirs forward deployed eng concept. Why it is, what it is, how works, what parts are important, how it is measured, etc.

Preparing for a meeting (or just researching someone)

If you're meeting executives or other people likely to have a public profile, this can be very valuable (I've redacted the concrete people for obvious reasons):

I am a vendor meeting CUSTOMER, give me research on the background of these people: * PERSON1@CUSTOMER.COM * PERSON2@CUSTOMER.COM ...

This has been very useful for me, especially since I am generally not a recurring presence in those meetings, so I don't have pre-existing deep relationships with everyone. Plus, in one instance, the LLM was able to explain that a weird email domain signified the person was part of an advanced R&D organization within the customer's company -- a useful fact.

The same approach applies for job interviews, coffee meetings, etc.

You might want to run the same thing on yourself and see what others are likely to see. Deep researching yourself is the 2025 version of googling yourself, and it of course relies on search engine results. Regardless, the LLM interpretation might be novel for you.