Tips to Get the Best Results from Top AI Deep Research Agents: ChatGPT, Gemini, and Perplexity
- Nishant
- Jun 29
- 5 min read
AI deep research agents are tools powered by AI reasoning models that can conduct in-depth, multi-step research on difficult topics like business, finance, science, policy, engineering, and more. These AI agents can automatically browse up to hundreds of online sources on your behalf, think through their findings, and create insightful multi-page reports in minutes. Every output generated by these AI agents is fully documented, with clear citations of its thinking, making referencing and verifying the information straightforward.
How does a AI deep research agent work?
What these AI deep research agents do is create a personalized multi-point research plan from your prompt, and then they autonomously search the web for the most relevant and up-to-date information, repeatedly reasoning through the data. Finally, after a few minutes, depending on the complexity of the research topic, they prepare a detailed custom research report.
What are the top AI deep research agents?
As of now, there are several AI deep research agents available for you to use from different companies:
OpenAI with ChatGPT deep research.
Google with Gemini deep research.
Perplexity with its own research tool.
There are more options like Liner, DeerFlow, xAI with Grok DeepSearch and DeeperSearch, and more, each with its own pros and specific use cases.
ChatGPT Deep Research (OpenAI)
ChatGPT deep research is available for all ChatGPT users to conduct autonomous, multi-source research. This AI deep research agent can search the web and can also analyze PDFs and spreadsheets to prepare a detailed report based on its findings. It functions like a dedicated research agent working in the background.
Pros:
Create complete and well-structured narrative reports.
Strong reasoning ability that can synthesize information from different sources.
Excellent for technical tasks, including code generation and data analysis.
Can process user-uploaded files for context.
Cons:
It can be slower than other AI deep research tools, with research tasks sometimes taking several minutes.
To get the best experience, users need to subscribe to ChatGPT Plus or Pro.
Perplexity Research
Perplexity Research conducts a multi-step search across the web with a focus on delivering factually accurate, concise summaries with clear attribution for every claim.
Pros:
Provides a source for nearly every claim, making verification straightforward.
It has a very fast response time.
Offers free users three free research queries with no quality difference.
Cons:
Less suited for deep, creative analysis; it reports what sources say rather than generating new insights.
While it can answer coding questions, it lacks a built-in environment to execute code.
Google Gemini Deep Research
As part of the Google ecosystem, Gemini's deep research capability creates a custom search plan and uses Google's own powerful search index to find high-quality, current information.
Pros:
Excellent for current events and trend-aware research.
Directly exports structured reports to Google Docs for easy editing and sharing.
Users can review and modify the AI's research plan before it runs.
All claims are backed by linked references for easy verification.
Cons:
The summaries can be more concise and less detailed.
It may not grasp the subtleties of highly specialized topics as well as other tools.
Practical tips to get the best results from top AI deep research agents:
To effectively use these AI deep research agents, a strategic approach is necessary. Professionals can get better, more reliable results by improving how they ask questions, verify information, and integrate the AI's output into their work. The following tips will give you a guide to get the best results from these top AI deep research agents.
Write Clear, Detailed Prompts:
The quality of the output you receive directly depends on the quality of your input. Provide the AI with as much context as possible and clearly outline the scope of the request, including key points to cover or constraints. Treating the AI like a research assistant and providing specific instructions helps all tools produce more relevant and accurate answers.
For example:
Instead of asking for "a report on the renewable energy sector," a more effective prompt would be, "Generate a 5-point summary comparing the market share of solar and wind power in the European Union for 2024, with citations from reputable industry reports."
Iterate and Refine Your Queries:
Deep research is an interactive process. Use follow-up prompts to go deeper into the information or change to a new line of questioning. All three major platforms, ChatGPT, Gemini, and Perplexity, can maintain context, allowing you to ask clarifying questions without having to restate your original request.
After receiving an initial report, you might ask the AI to "expand on point 3 with more recent data" or "explain this finding for a non-expert." If the initial results are too broad, break your question into smaller, more manageable sub-questions.
For example:
First, ask for market size data, and then, in a subsequent prompt, ask the AI to analyze the key growth drivers based on that data. This iterative approach can often help find more subtle insights than a single, broad question.
Validate and Vet the Outputs:
Never take an AI's answer at face value. Always verify the key facts, especially when dealing with important data. Each of these deep research tools gives citations, making it easier to cross-check the information.
For ChatGPT and Gemini, click on the source links provided in the report. For Perplexity, you can use its Sources option or export the results to a PDF to review the sources.
A good practice is to open the cited articles and confirm that the AI's summary is accurate and according to the source material. This "trust but verify" approach is important. Strategies for validation include cross-referencing facts with other sources or using academic search engines like Google Scholar for confirmation, and do not shy away from discarding any AI-generated claim that lacks a proper citation.
Choose the Right Tool for the Task:
Each AI deep research agent has its own strengths, and the choice of deep research tool for your specific research work can lead to better outcomes.
Strategy and Market Reports:
For up-to-the-minute market data, financial statistics, news, and trends, Gemini and Perplexity are excellent choices due to their live web access. You could ask Gemini to brief you on "recent regulatory changes in the fintech industry" and then use ChatGPT to synthesize those factual findings into a more detailed narrative or a slide-ready presentation.
Technical Deep Research:
When your work involves technical analysis or coding, ChatGPT is often the most suitable tool. It can parse complex documents and codebases that you upload, and it can generate or validate code and algorithms.
While Gemini is very capable, and Perplexity can fetch relevant research papers or code snippets from the web, ChatGPT's code execution capabilities make it better suited for in-depth technical work.
Competitive and Industry Analysis:
Gemini is more well-suited for this type of research, as its connection to Google's search index allows it to pull in recent product launches, news articles, and industry reports.
You can have Gemini map out a competitor's recent announcements, cite the sources, and then ask it to summarize the implications for your company. Perplexity can also be used to find facts about specific companies or products with ease and accuracy, thanks to its quick reference capabilities.
In Conclusion:
AI deep research agents are powerful and capable ways for professionals to plan, execute, and summarize research tasks and quickly create detailed reports. However, these AI research tools are most effective when you use them strategically. You need to be clear while providing good context, fact-checking the generated information, and choosing the right AI deep research agent can help you get the best results. AI agents are autonomous, but they are not perfect yet; therefore, be more strategic to ensure accuracy and reliability in the final output.
