I love to immerse myself in learning about new things and fall into research rabbit holes, but sometimes I just need a quick and efficient answer to a question or concise guide for a task. If I am trying to find out how long the chicken or if Pluto has been reinstalled as a planet, I want a brief list of bullet points and a simple yes or no.
So, although Chatgpt's deep research function has proven to be an incredible researcher who is great when I want to immerse myself in a topic, I have not become my predetermined tool with the AI chatbot. The AI model database, as well as your search tool, solve almost any daily question or problem that can do it. I do not need a formal report on how to make a meal that takes 10 minutes to compile. But, I find the integral responses of deep investigation viscerally attractive, so I decided that it was worth comparing it with the Standard Chatgpt model (GPT-4O) and giving some indications that could imagine submitting by a whim or with little long-term need.
Beef Wellington
For the first test, I wanted to see how both models would handle a classic and somewhat intimidating recipe: Beef Wellington. This is not the type of dish you can get together on one night. It is a process that requires a lot of time and multiple steps that requires patience and precision. If there was ever a meal in which a deep investigation could be useful, this was all. I asked both models: “Can you give me a simple recipe for beef Kosher Wellington?”
Chatgpt regular responded almost instantly with a direct and well structured recipe. He listed ingredients in clear measurements, broke the process in manageable steps and offered some useful tips to avoid common difficulties. It was exactly what I needed in a recipe. Deep Research took ten full minutes and had a very long and complex mini cook focused on the plate. I had multiple versions of Beef Wellington, who adhered to my specific requests, but I went from a method inspired by Jamie Geller to a traditional preparation of the nineteenth century with some substitutions. That does not count additional suggestions on decorations and an analysis of various types of puff pastry and its steak proportions. If I am honest, I loved it as an obsession of trivia. But, if I really wanted to make the dish, it looked too much to those recipe blogs where you have to move beyond the history of someone's lifetime just to reach the ingredient list.
Television time
For the second test, I wanted to see if a deep investigation could help me buy a TV, so I kept it simple with: “What should I consider when buying a new TV?”
Chatgpt regular gave me a quick and clear answer. He broke things in the size of the screen, the resolution, the type of visualization, the intelligent characteristics and the ports. He told me that 4K is standard, 8K is excessive, OLED has a better contrast, HDMI 2.1 is excellent for budgetary games and issues. I felt that I had a decent understanding of what to look for, and I could have easily entered a store with that information.
Deep Research had his usual additional questions about what is important for me, but this time it was faster, only six minutes before delivering a complete report on several televisions. Except that instead of a simple list of pros and cons, I obtained many unnecessary details about things such as panels Oled vs. QLED, the reason why TV update rates affect video games and the impact of compression algorithms on transmission quality. Again, all this was incredibly informative, but completely unnecessary for my purposes. And unlike Beef Wellington, I will not continue to return to the TV purchase guide in a semi-regular way.
Telescope look
For the final test, I decided to get a little more academic in the light of my recent decision to follow astronomy more seriously as a hobby. I still kept it brief, I asked, “How does a telescope work?”
Chatgpt regular responded instantly with a simple and digestible response. Telescopes gather and magnify the light using lenses (refraction telescopes) or mirrors (telescopes that reflect). It briefly touched the increase, resolution and power of light collection, which facilitates understanding without being too technical.
Deep Research gave me a guy report that I could have written in high school. After asking how technical I wanted my answer, and I replied that I did not want it to be a technician, I waited about eight minutes for a long discussion of the optics, the development of different types of telescopes, including radiolelescopes and the mechanisms behind how they all work. The report even included a guide on the purchase of its first telescope and a discussion about atmospheric distortion in terrestrial observations. I was answering questions that I hadn't asked. It is true that he could do it at some point, so the anticipation of monitoring consultations was not a great negative in this case. Even so, a couple of mirrors would have been well at the time.
Deep thoughts
After executing these tests, my opinion on deep research as a powerful AI tool with impressive results remains, but I feel much more aware of its excesses in the context of the regular use of Chatgpt. The reports generated are detailed, well organized and surprisingly well written. For an random information tour, it is quite good, but much more frequently I only need an answer, not a thesis. Sometimes, a shallow sauce is preferable to deep immersion.
If the regular chatpt approach is necessary and does in seconds what requires deep investigation several minutes and a lot of unnecessary context to provide, that will be my preference 99 times one hundred. Sometimes, less is. That said, the Deep Research purchase tips would be excellent for a purchase much larger than a TV, such as a car or even when looking for a house. But for everyday things, deep research is doing too much. I do not need a reaction engine for an electric scooter, but, for a transcontinental flight, that reaction engine is good to have at hand.
