This is essential to speed up content production routines, for example, which require a considerable amount of research.
But now we’re in 2025, and the question arises: what has actually happened? Is AI research truly feasible? And what research modalities already exist?
That’s what we’re going to talk about in today’s text. Let’s go together?
Ways to Do Research with AI in 2025
AI research already happens today in a few different forms.
Before we delve deeper into the text’s topic, which deals with advertising research for content production, we need to understand the possibilities of AI as a whole.
In fact, we’ve already written some very comprehensive articles on the subject. You can access them below:
What to expect from Artificial Intelligence in 2025?
Artificial Intelligence in country email list Digital Marketing – 30 examples
Customer acquisition with Artificial Intelligence – step by step
How to use Artificial Intelligence in Each Segment
The point is that we first need to contextualize what the ways of actually doing research with AI are because we will use these research modalities throughout the text.
For example, academic research with AI is not advertising research. However, advertising research can use resources from academic research.
Understanding everything that can be researched with the help of AI, and what can be automated within those searches, is essential for content production.
Below, we’ll discuss the X types of research that AI will enable in 2025. Let’s learn more:
Training AI with your Data
Let’s first demystify one of the most interesting (and complex) uses of AI: RAG, or Retrieval Augmented Generation.
The way most popular AIs work today is quite simple: they have a large database, and they use these databases as context for creating responses.
AIs operate through three main points: the user prompt , the database, and the response generation process, which uses a probabilistic model.
All of this is a result of Natural Language Processing. More on these points in the link.
But what if it were possible to replace this AI database with our own information?
Using RAG, this is entirely possible, but quite complex to perfect.
It’s now possible to start testing at least one platform developed exclusively for RAG: Nvidia ChatRTX.
This platform allows you to add your database to AI in a very simple way, with low-code or even, in simpler cases, no-code.
It is also possible to do this work directly in ChatGPT, but it is a little more complex.
At the end of the text, we provide a quick tutorial on how to configure this in ChatGPT. It will require at least one AI-specialized developer, be careful when handling cheap apparel but the work is not remotely complex.
For now, let’s move on to ways to conduct research with AI:
Research in Academic Databases with AI
The point is that through RAG you can do all kinds of research you want, as long as you have the databases you’re interested in saved in documents to feed the AI.
But those who don’t have much knowledge in AI development — which, again, isn’t that difficult and is completely “learnable” — require simpler tools.
One way to achieve this simplicity is by using ChatGPT plugins. These aren’t plugins you install, but versions of ChatGPT that are already plugged into academic databases.
One such tool is SciSpace. It’s essentially the ChatGPT environment, but with an added plugin for searching academic databases. Click here to access it:
The official website;
The ChatGPT plugin;
Another interesting AI is Scite , but it comes from a more hands-on tradition. With it, you can create a list of articles and use them for research and citations.
At this point, it’s not so much a Q&A AI, phone database but rather functions primarily as a content aggregator based on what you’re searching for.
There are several other AIs on the market specifically geared towards academic research, each with specific characteristics related to the work you will be doing.
It’s important to test, test, and keep testing until you find what makes the most sense for you.
AI that Creates a Company Database
This is one of the least easy uses to find.
The idea is this: you want to build a company ChatGPT, capable of answering both complex and simple questions.
For example: you want an AI capable of telling you about the data from the company’s latest quarterly report, including answering specific questions, such as “which teams hit their target this quarter?”.
At the same time, you also want simple information, such as “who are the members of manager Daniela’s team?”
This is one of the most complex AIs to create. It doesn’t function like RAG, but rather more like a search engine, an ESS—Enterprise Search Service.
In RAG, your database is analyzed to provide answers, but it is the AI that provides these answers within the probabilistic model — that is, prone to errors.
In business, information needs to be 100% accurate, or AI isn’t a worthwhile resource.
Today, the AI that comes closest to doing this job is Amazon’s Kendra , but its costs are quite high.