I can get answers from chatgpt, but deep research gives me a complete thesis that I will never need.

I love diving into learning new things and falling down research rabbit holes, but sometimes I just need a quick, efficient answer to a question or a brief guide to a task. If I’m trying to figure out how long a roast chicken is or whether Pluto has been reinstated as a planet, I want a short list of bullet points and yes or no.

So while ChatGpt’s Deep Search feature has proven to be a great, great researcher when I want to dive into a topic, I haven’t done that by default with the AI ​​chatbot. The AI ​​model’s database, as well as its search tool, solves any everyday question or problem I might throw at it. I don’t need a formal report on how to make a meal that takes 10 minutes to assemble. But I find the comprehensive answers from Deep Search incredibly engaging, so I decided it was worth comparing it to the standard ChatGPT model (GPT-4O) and giving it some prompts that I could imagine on a whim or with little long-term need.

Beef Wellington

For my first test, I wanted to see how both models would handle a somewhat intimidating classic recipe: beef Wellington. This isn’t the kind of dish you can just throw together on a weeknight. It’s a time-consuming, multi-step process that requires patience and precision. If ever there was a meal where deep research could come in handy, this was it. I asked both models: “Can you give me a simple recipe for Kosher Beef Wellington?”

The regular ChatGPT responded almost immediately with a straightforward, organized recipe. It listed the ingredients in clear measurements, broke the process down into manageable steps, and offered some helpful tips for avoiding common pitfalls. It was exactly what I needed in a recipe. The deep research took a full ten minutes, and the Mini Book was pretty complicated on the dish. I had multiple versions of Beef Wellington, which met all my specific requests, but ranged from a Jamie Geller-inspired method to a traditional 19th-century preparation with a few substitutions. That’s not counting the additional suggestions for toppings, analysis of different types of puff pastry, and butter-to-style ratios. If I’m honest, I loved it as a piece of trivia obsession. But, if I wanted to actually make the dish, it felt a lot like one of those recipe blogs where you have to scroll through someone’s life story just to get to the ingredient list.

TV time

For the second test, I wanted to see if deep research could help me buy a TV, so I kept it simple with: “What should I consider when buying a new TV?”

The regular chatgpt gave me a quick and clear answer. It broke things down into screen size, resolution, display type, smart features, and ports. It told me that 4K is standard, 8K is overkill, OLED has better contrast, HDMI 2.1 is great for gaming, and budget matters. I felt like I had a handle on what to look for, and could have easily walked into a store with that information.

The deep dive had the usual additional questions about what’s important to me, but it was quicker this time, with just six minutes to go before I could do a full report on several TVs. Beyond a simple pros and cons list, I got a lot of unnecessary detail about things like OLED vs. Qled panels, why TV refresh rates affect video games, and how compression algorithms affect streaming quality. Again, all of this was incredibly useful, but completely unnecessary for my purposes. And unlike Beef Wellington, I won’t be returning to the TV buying guide on a semi-regular basis.

telescope view

For the final exam, I decided to get more academic in light of my recent decision to pursue astronomy more seriously as a hobby. I asked, I asked, I asked, How does a telescope work?

The regular chatgpt responded immediately with a simple, digestible answer. Telescopes gather and magnify using either lenses (refracting telescopes) or mirrors (reflecting telescopes). He briefly touched on magnification, resolution, and light-gathering power, making it easy to understand without getting too technical.

The deep dive gave me a report of the sort I might have written in high school. After asking how technical I wanted my answer to be, and I replied that I didn’t want it to be technical, I waited about eight minutes for a long discussion of optics, the development of different types of telescopes, including radio telescopes, and the mechanics behind how they all work. The report even included a guide to buying your own Olsscope and a discussion of atmospheric distortion in ground-based observations. It answered questions I hadn’t asked. Granted, I might do that at some point, so anticipating follow-up inquiries wasn’t a huge negative in this case. However, a few sentences about mirrors were fine for now.

deep thoughts

After running these tests, my opinion of Deep Search remains that it is a powerful AI tool with impressive results, but I feel more aware of its limitations in the context of regular chatgpt use. The reports it generates are detailed, well-organized, and surprisingly well-written. For a random round of interesting information, it’s great, but more often than not, I just need an answer, not a thesis. Sometimes a shallow dive is better than a deep dive.

If ChatGPT’s usual approach was accurate and did in seconds what Deep Research takes several minutes and a lot of unnecessary context to provide, that would be my preference 99 times out of a hundred. Sometimes, less is more. That said, Deep Research’s shopping advice would be great for a much bigger purchase than a TV, like a car, or even when looking for a house. But for everyday things, Deep Research does a lot. I don’t need a jet engine for my electric scooter, but for a cross-continental trip, it’s nice to have a jet engine on hand.

• I tried deep research on ChatGpt, and it's like a very smart but slightly absent librarian from a children's book.
• I tried to dig deep into the confusion and it does not live up to the potential of ChatGPT research.
• لقد استبدلت قائمة المهام الخاصة بي بميزة مهام ChatGpt وقمت بتغيير طريقة التخطيط لحياتي تمامًا