Deep Research is one of those offerings that you have to try for yourself to really understand what it is all about. At least, that’s how it was for me.
Right after my first experiment with it, I was amazed and thought: This could change my work almost as much as ChatGPT itself has already done.
This time it’s not about writing or tasks around planning and publishing content. This time it’s about the research phase itself.
In this post, I’ll show you what deep research is, how it works, how you can use it, what its shortcomings are, and what alternatives are available.
I write this from my perspective as a trained journalist, experienced online publisher, and seasoned content marketer.
What is ChatGPT’s Deep Research?
Unlike a simple AI chat interaction or a quick search, Deep Research functions as an autonomous research assistant, going through multiple steps to gather information from sources, and to shape them into structured reports with lots of footnotes.
I see it as the third step in a progression of how ChatGPT has evolved as a research tool for me as a writer and content creator:
1. ChatGPT by itself: a basic starting point
At its core, ChatGPT has always been useful for understanding concepts and exploring ideas. It allows me to discuss topics, get general explanations, and summarize broad subjects. However, in its earliest versions, it relied only on pre-trained knowledge, meaning it couldn’t retrieve fresh or external information.
Today, with web browsing enabled, ChatGPT can look up real-time data. However, this still works best for quick overviews or wrapping your head around a topic, rather than serious research.
2. ChatGPT Search: a search engine with some smarts
The introduction of ChatGPT Search was a big step forward. ChatGPT acts here like a search engine assistant, collecting and summarizing information from web pages. This made it far more useful for fact-finding, allowing me to get sources, figures, and relevant links in one place.
This is helpful for finding key insights quickly, but it still has limits:
- It typically pulls from a small number of sources.
- The summary is often high-level, not in-depth and structured.
- It requires me to actively direct the research process.
3. ChatGPT Deep Research: multi-step analysis and in-depth reports
Deep Research takes things to a new level:
- Multi-step research: It iterates on its own, refining the search, cross-referencing information, and structuring a complete report. You can watch it during that process, because it documents it live.
- Hard-to-reach data: It looks beyond the most obvious sources, often pulling from academic papers, industry reports, niche blogs, and expert commentary. That’s especially true when your prompt nudges it that way. More about good prompts for Deep Research queries below.
- Structured report: You get a detailed, organized document with citations, key takeaways, and often multiple perspectives on the topic. And again: The better your prompt, the better the result.
It is an AI research assistant that can save hours of work, freeing you to focus on strategy and creative tasks.
Pricing
If you have a ChatGPT Plus account ($20/month), you can use Deep Research 10 times per moth. With ChatGPT Pro ($200/month) it’s 120 times.
While it doesn’t seem much to be able to use Deep Research only 10 times, it can be very useful if your prompt is well crafted.
Use cases for Deep Research in content marketing
But what can Deep Research really do for you? Here are some ideas.
SEO research and content strategy
Keeping up with SEO trends and best practices is a full-time job on its own. Deep Research can help by:
- Summarizing the latest search algorithm updates, ranking factors, and case studies of high-performing content.
- Analyzing keyword trends and user search behavior by pulling insights from industry reports, SEO blogs, and online discussions.
- Identifying content gaps by scanning competitors’ blogs, Q&A sites, and forums for frequently asked questions that haven’t been addressed well.
This allows content marketers to build a strategy that is backed by current data, rather than relying on guesswork or outdated best practices.
Audience and market insights
Instead of manually reading through social media discussions, market research reports, and industry news, you can ask Deep Research to:
- Aggregate consumer opinions on a specific topic or product.
- Identify trends in how people talk about a subject across different platforms.
- Provide data-driven insights into audience pain points, needs, and interests.
For example, if you’re launching a sustainability-focused product, Deep Research can analyze how consumers are discussing sustainability in fashion, what concerns they have, and what messaging resonates with them.
Competitive analysis
Knowing what your competitors are doing can give you a strategic edge. Deep Research can provide insights by:
- Summarizing competitors’ content strategies, including their most important topics and formats.
- Analyzing their brand messaging and how it evolves over time.
- Identifying gaps in their content that you can take advantage of.
Instead of manually reviewing multiple websites, blog posts, and press releases, Deep Research can compile this information into a structured report that makes it easy to see where opportunities lie.
Content creation and journalism
Writers and content creators can also benefit from Deep Research in several ways:
- Quickly gathering well-sourced data and citations for articles.
- Generating a structured research document to use as a foundation for an in-depth piece.
- Summarizing complex topics in a way that is easy to digest.
For example, a journalist working on an article about AI ethics could ask Deep Research to compile key arguments from academic papers, industry discussions, and expert opinions. This allows them to spend less time finding sources and more time crafting the story.
My personal view and experience
Deep Research doesn’t replace human expertise. More about that below. But it definitely feels like (another) super power. I’ve never head a research assistant at my disposal. Now I have a tool that does a lot of the heavy lifting for me.
One concrete and recent instance from my own work: I used Deep Research to find facts and figures for an article about sustainable web hosting I was working on. It found relevant statistics, regulatory details, and interesting examples. It would have taken me way too long to find this information the old-fashioned way. It improved the article by a lot. Of course I double checked everything. That was easy enough, because Deep Research did not only link to the sources, but also highlighted the respective passage in the text.
Limitations of Deep Research
Deep Research is a powerful tool, but it’s not without its flaws. While it can automate much of the research process, content marketers need to be aware of its limitations.
Requires human oversight
Deep Research gathers and synthesizes information, but it doesn’t think critically like a human researcher. It may not always prioritize the most relevant or accurate details. You still have to apply your own judgement.
Occasional inaccuracies and hallucinations
AI models can misinterpret data, overgeneralize, or even fabricate information. While Deep Research is designed to be more reliable than standard ChatGPT responses, it still can make errors. Therefore, check sources and cross-reference information.
Struggles with recent developments
Since Deep Research relies on publicly available web sources, it might miss breaking news or the most recent updates on a topic. If you’re researching an evolving issue you may need to supplement Deep Research with manual searches.
Doesn’t always assess credibility correctly
Deep Research retrieves information from a variety of sources, but it doesn’t inherently distinguish between highly credible sources and lower-quality ones. It might cite a niche blog with limited expertise alongside a reputable industry report without indicating which is more authoritative. It’s up to you to assess the reliability of each source.
Can miss nuance and context
AI doesn’t understand topics the way humans do. It might pull together facts without recognizing their deeper significance or context. For example, it could summarize multiple opinions on a subject but fail to highlight a critical contradiction between them. That’s why human expertise is still needed to interpret and apply the research effectively.
Not a replacement for strategy and creativity
Deep Research can surface data, insights, and trends, but it won’t create a compelling content strategy for you. It can help answer what is happening, but not why it matters or how to act on it. That’s where your creativity and marketing instincts come in.
My take on it
It feels similar to what I’ve seen with ChatGPT in general: It looks like it’s working so well that you can just automate the whole process. But, please, don’t let AI do work on its own. It’s a partner, not a substitute for a human. You should still keep your hands on the wheel at all times.
If you keep that in mind, it’s still a fascinating and powerful new tool.
Tips for better prompts
Similar to ChatGPT and other current AI tools: A good prompt can make all the difference. It sometimes can feel like too much work. But from my experience, that’s only at the beginning of your learning curve.
Write clear and specific prompts
Deep Research works best when given detailed and well-structured prompts. Instead of a vague question like “What are the latest SEO trends?”, a more effective prompt would be:
“Analyze the latest SEO trends based on search algorithm updates, expert blog posts, and industry case studies. Focus on emerging ranking factors, content strategies, and technical SEO best practices from the past 12 months.”
The more precise your request, the more relevant and actionable the research will be.
Use the clarification step
Deep Research often asks follow-up questions to refine its research. Take advantage of this by providing additional details. If you’re researching audience behavior, for example, specifying the region, industry, or platform focus will improve the quality of the results.
Specify source types or date ranges when needed
If freshness or source quality is important, include that in your prompt. For example, you can request only studies from the past two years or ask for insights from expert blogs rather than general news sources. This helps avoid outdated or low-quality information.
Ask for multiple perspectives
AI models tend to present information as a single, unified summary. If you want a more nuanced view, explicitly ask for opposing viewpoints or different angles on a topic. For example, instead of asking “What is the best content marketing strategy?”, you could ask:
“Summarize different expert opinions on the most effective content marketing strategies. Include at least two contrasting viewpoints and examples of brands applying these approaches.”
Use iterative refinement
If the first response isn’t detailed enough, ask follow-up questions or request additional sources. Deep Research can refine its findings based on your feedback, making it a more interactive research tool rather than just a one-time query.
Alternatives to Deep Research
Last but not least: There are several alternatives to ChatGPT’s Deep Research, each with its strengths and trade-offs.
Google Gemini
Google’s AI research tools, including Gemini, are integrating deep web searches and citations. While still developing, Gemini could have an edge in pulling from Google’s vast index, including Scholar and News.
Perplexity AI
Perplexity AI is an AI-powered search engine and useful for quick research. Shortly after OpenAI unveiled Deep Research they countered with their own feature of the same name. It’s cheaper and even available for free accounts. My experience: It is more detailed and in-depth than a general Perplexity search, but it falls short of ChatGPT’s results.
You.com Advanced Research & Insights
And just recently the AI platform You.com presented their take on this new category with their Advanced Research & Insights offering. One if its standout capabilities: It is supposedly able to process approximately ten times more sources while operating three times faster than the competition.
Open-source research tools
For those who prefer customizable and privacy-respecting solutions, open-source AI tools and workflows allow for tailored multi-step research. These can be cost-effective but require technical setup and lack the ease of use of Deep Research for non-technical users. One example is Open Deep Research by Hugging Face.
My opinion on Deep Research
As you might have guessed already: I do like Deep Research as a new tool for my work despite its shortcomings. It is mainly meant for professional researchers, scientists, and the like. But it can also be very useful for writing and content creation as well as content strategy. I feel like I’ve only scratched the surface of what it can do and how I can use it.
It’s also fascinating to see how OpenAI created a new category of AI service–again. Others have followed and this competition will hopefully lead to ever better tools and more choices going forward.
Sources: Datacamp, tech.co, OpenAI, Contentgrip, Science Alert, Stradiji