I I recently conducted a research on the state of AI agent solutions on the market, which is growing every day and has become the de facto trend in the AI space at the moment. For this purpose, there are so many ways to do an AI-powered research like this:
- Using ChatGPT web search
- Using Perplexity
- Using Gpt-Researcher with Tavily
- And a few more.
They are great for getting a good overview of research material relevant to your topic. But the reality is sometimes a little different: Depending on the context, you may only want to consider curatedreliable sources for your research, depending on factors like trust, compliance, etc.
For example, if I were comparing two hosting providers such as Vercel and Railway, I would not necessarily rely on technical details provided by a third party, I would prefer sources directly from the provider, or at least consider them more relevant.
Approach to Curated Research
For a more fine-tuned approach (taking AI agents as an example), I’d usually do something like this:
- Do a general overview search to find suitable sources (e.g., Wikipedia, Hugging Face, LinkedIn posts).
- Check these sources and pick out the relevant parts by reading through them and selecting where I want to dig deeper—like choosing a list of open-source agent solutions I might explore.
- Check the vendor or GitHub repositories of those solutions directly.
Sometimes the sources aren’t just websites. They could be YouTube videos, PDF files, and so on. We have to consider different formats accordingly.
In the past, I often didn’t go beyond the initial “overview” stage of research. Most of the deeper exploration was done manually.
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