AI Deep Research: What it is and when to use it | Xyclos
- Carlos Altamirano
- 1 day ago
- 6 min read
Do you use AI? Before continuing, learn this about Deep Research.
In a digital environment where decisions are accelerated and tools evolve daily, mastering deep research with AI isn't an option; it's a strategic advantage. In this article, we explore how to use Deep Research with language models like ChatGPT to transform the way you work, decide, and create. And most importantly, when to use it and when not to.
With LLM companies continually releasing new language models with different features, it's sometimes difficult to determine which one to use.
For example, OpenAI 's ChatGPT currently has the following in the Plus version:
GPT-4o Ideal for most tasks ✅
o3 Use advanced reasoning
o4-mini Faster at advanced reasoning
o4-mini-high Ideal for programming and visual reasoning
GPT-4.5 PREVIEW Ideal for writing and exploring ideas
GPT-4.1 Ideal for rapid programming and analysis
GPT-4.1-mini Faster, for everyday tasks
And once we select one of them, by clicking on Tools we have:
Create an image
Search the web
Writing programming code
Carry out the investigation thoroughly
Table of Contents of this blog
And in what tasks or activities can we use LLMs?
In general we can use them for:
Creativity
Search for information
Programming
Data analysis
Creating images
In-depth research
And in more detail let's look at some specific use cases of AI:
Creativity Writing, idea generation, advertising copy, storytelling.
Effective communication
Writing emails, preparing presentations, speeches, and scripts.
Information Search
Up-to-date queries, fast and accurate web search.
Programming
Code creation, analysis, debugging, and optimization.
Data Analysis
Interpretation, advanced calculations, report creation, insight extraction.
Automation and productivity
Automation of repetitive tasks, process assistance, and time optimization.
Decision support
Evaluation and comparison of alternatives, support in decision-making.
Creating Images
Visual generation from specific descriptions or prompts.
Education and learning
Clear explanations, educational content generation, assessment creation, and feedback.
Digital marketing and SEO
Search engine optimization, promotional copy creation, email marketing.
Technical support
Technical troubleshooting, step-by-step instructions, technical support.
Deep Research
Detailed research, exhaustive analysis, development of complex and technical topics.
So we can see that they can help us with practically everything we need to do. However, we must use them judiciously and not trust what they say 100%. We must always validate and reflect on the answers we are given. With this in mind...it's best to use them!
But let's get to the topic of in-depth research.
In these two and a half years, I've seen how the LLMs evolve, continually improving. For example, before, they had many hallucinations, now not as many, or almost none. The same has happened with their reasoning process: before, let's say, they didn't reason, and now, this year... they reason and reason more. They're like children growing up, maturing at a rapid pace.
Human biological time is not the same as that of AIs, because at the rate they're going, it's as if they're now like 12 or 15-year-olds, and they achieved that in just two and a half years; that means that in another two or three years they'll be adults and very, very well prepared... and from then on... I have no idea... I only know that we must prepare for a very special and different future, otherwise they would have been invented.
What is Deep Research with AI?
Deep Research is like having an expert research assistant working for you. Instead of giving you a quick answer based on its knowledge, the AI goes out and finds up-to-date information, analyzes it from multiple angles, and delivers a comprehensive report just like a professional researcher would.
It is an additional layer to the neural interaction process that involves analyzing and dividing into parts, analyzing each part individually, determining relationships between the parts, and continuously iterating to ensure a correct analysis, presenting verifiable citations, establishing coherent reasoning, and searching for references or sources of information on its own; that is, giving its best, similar to the techniques scientists use when preparing their papers or research results.
So, can we use deep research for everything?
No.
If we ask an AI simple queries like:
What is the capital of Uganda?
What does ROI mean in marketing?
How to calculate 21% VAT?
Or to help us with routine tasks such as:
Writing a follow-up email to a client
Create a task list for a project
Write a basic product description
Translate a short text
We don't need to activate deep investigation.
On the other hand, if we need to research something for a job or project and it will require us to investigate and search in several places, then we should activate Deep Research.
Let's look at some examples of WHEN we SHOULD use Deep Research:
Professionals
A lawyer preparing a case on cryptocurrency regulations in different countries
A doctor researching emerging treatments for a rare disease
A consultant analyzing market trends in renewable energy for a client
A journalist investigating the social impact of social media on teenagers
Academics
Master's student writing on "The Impact of AI on Financial Sector Employment 2019-2024"
Researcher comparing public mental health policies in 5 Nordic countries
Thesis on "Evolution of Sustainable Agricultural Techniques in Latin America"
Businessmen
Competitor analysis before launching a healthy food delivery startup
Market research to expand e-commerce to new countries
Feasibility study for implementing remote work in a traditional company
By activating deep research, the model will search for sources on the Internet, review concepts, news, and other research, a task that will save us a lot of time if we did it manually.
And after completing all the research required for the topic, we will receive a perfectly organized and formatted document for our use.
And as always: validate...validate...validate before using or publishing it.
How to Effectively Validate Deep Research Results
Source Verification
Review citations : Go directly to the cited sources to confirm that they exist and say what the AI claims.
Evaluate credibility : Prioritize academic sources, recognized institutions, established media over personal blogs or sites without an author
Date sources : Make sure the information is current, especially on rapidly evolving topics like technology or medicine.
Contrast with Multiple Sources
Triangulation : Find at least 2-3 independent sources that confirm the main data
Consult experts : For specialized topics, check with professionals in the area
Review primary sources : Whenever possible, go to the original source (study, research, official document)
Logical Coherence Analysis
Evaluate the connections : Do the arguments follow a logical sequence?
Detect contradictions : Look for information that contradicts itself within the same document
Question conclusions : Do the conclusions follow naturally from the data presented?
Data and Statistics Verification
Check numbers : Recalculate percentages, totals, and comparisons whenever possible
Temporal context : Verify that the statistics correspond to the period mentioned
Data source : Confirm that the data comes from reliable organizations (INE, World Bank, etc.)
Warning Signs That Require Extra Validation
Categorical statements without nuances ("always", "never", "everyone")
Very precise statistics with no clear source
Information that contradicts your prior knowledge
Sources you can't find or verify
Conclusions that seem too convenient for your argument
Practical Tools for Validation
Google Scholar : To verify academic studies
Fact-checking sites : Snopes, PolitiFact, AFP Factual
Specialized databases : PubMed for medicine, JSTOR for academics
Official pages : Government sites, international organizations
Step-by-Step Validation Process
Read critically : First read through, identifying key claims
Check mark : Underline statistics, dates, names, key statements
Check systematically : Dedicate 15-20% of total time to checking
Document findings : Write down what you checked and what you found
Adjust the content : Correct, clarify or delete unverifiable information. Here you can use Microsoft Word to read the document to you and, with the Review/Track/Track Changes option enabled, make annotations or comments.
We must also keep in mind and be aware that activating deep search consumes more computing and energy resources. If we use it wisely, everyone wins.
What other language models (LLM) can be used for deep research with AI?
Claude (Anthropic)
Perplexity (Perplexity AI)
DeepSeek (DeepSeek)
Grok (xAI)
Copilot (Microsoft/OpenAI)
ERNIE (Baidu)
Frequently Asked Questions about Deep Dive with AI
What is Deep Research in AI?
It is an advanced technique that emulates the analysis of a scientific researcher: it breaks down the problem, examines its components, finds relationships, and consults multiple sources to deliver a coherent and well-founded analysis.
When should you activate the deep dive feature?
When the task requires in-depth analysis, such as technical writing, thesis, specialized papers, or complex topics. It is not necessary for simple queries.
Does deep dive research consume more resources?
Yes, it requires more computing power, so it is recommended to use it judiciously.
Comments