ChatGPT’s Deep Research tool is a breakthrough for users who need detailed, multi-source insights without spending hours scrolling through the internet. Whether you’re researching a product, exploring business trends, or diving into technical topics, it provides high-quality summaries in minutes.
But while the feature is powerful, it’s not perfect. As someone who frequently uses Deep Research, I’ve noticed areas where it could be more intuitive, customizable, and user-friendly. In its current form, it’s like a high-powered engine that still needs tuning to perform at its absolute best. Here are 8 features that could dramatically improve ChatGPT’s Deep Research tool—and make it a true game-changer for every kind of user.
One of the most exciting additions OpenAI could make is letting users set custom parameters before launching a Deep Research request. Right now, you rely on follow-up questions to guide ChatGPT’s focus. But imagine if, before running the research, you could adjust filters similar to shopping websites.
You could select:
It would make research much more focused and accurate. Instead of sifting through generalized summaries, you’d get exactly what you’re looking for—without having to clarify or redirect in follow-up prompts.
ChatGPT tries its best to organize data effectively, sometimes providing bullet points, other times writing dense paragraphs or creating tables for comparisons. While this variety is great, the lack of user control over the layout can lead to frustration.
There are moments when a table would make information clearer, yet the tool responds in paragraph form. And while you can request a specific format using prompts, the results can be inconsistent.
The ideal improvement? Give users a simple option to choose how they want the final information presented—whether that’s in a table, bullet list, or full- text explanation. A toggle menu or formatting preference setting could dramatically improve clarity and usability.
For power users and professionals, the current Deep Research allowance feels too limited. Most users on the ChatGPT Plus plan get only 10 Deep Research requests per month, and that can disappear in just a couple of days—especially if you use the feature to support regular work or academic needs.
This restriction feels at odds with the value offered by a paid subscription. Expanding this to 15–20 monthly queries for Plus users and even more for Team or Pro plans would strike a better balance. It would also encourage higher- tier signups by offering tangible, day-to-day benefits. It isn’t about giving away more for free—it’s about ensuring paying customers get what they truly need out of the platform.
ChatGPT is used for a wide range of tasks—from grocery planning to mental health journaling—and Deep Research chats often get buried in the clutter. There’s currently no way to visually distinguish a conversation that used Deep Research from regular chats.
A dedicated section for Deep Research results (or at least a unique icon or tag) would go a long way in keeping things organized. Users should be able to revisit past research without scrolling through unrelated threads. This simple interface improvement could transform how people use and navigate ChatGPT’s workspace, especially for users juggling multiple projects or topics.
Custom GPTs are incredibly powerful for creating specific tones, voices, and functionality within ChatGPT. Unfortunately, you can’t currently use Deep Research with them—and that’s a missed opportunity.
Imagine pairing a research request with a GPT tailored for financial analysis, medical summaries, or content creation. The added context from a custom GPT could refine Deep Research outputs significantly.
While there may be technical hurdles in combining both features, even partial integration would unlock huge potential. Custom GPTs could guide the type of language used, the depth of explanation, and the relevance of the findings—making the results more personalized and aligned with your needs.
When ChatGPT finishes a Deep Research task, the response is often massive—a wall of text that can feel overwhelming. Even though the content is valuable, reading through large blocks of information can be a challenge, especially on mobile screens.
Breaking down the response into smaller, more digestible sections would be a smart fix. Clear headings, more frequent paragraph breaks, or collapsible sub- sections would help users identify the most important takeaways faster. For visual learners or those short on time, skimming becomes easier.
Not all sources are created equal. Some sites are filled with misinformation or overly biased opinions, and when you’re doing serious research, you don’t want to rely on them.
That’s why adding an “exclude domain” option would be a powerful upgrade. Users could input URLs or domains they don’t want to consider in their research—similar to blocking sites in parental controls or productivity apps. While this might increase processing time, the benefit is clear: more trusted, accurate, and personalized results.
ChatGPT’s Voice Mode is excellent for general interaction, but it hasn’t yet been extended to the Deep Research tool. Integrating voice input and audio summaries could be a game-changer, particularly for auditory learners or users with visual impairments.
Imagine being able to say, “Research the impact of climate change on coral reefs,” and then hearing a natural, spoken explanation of the findings while walking or commuting. Bonus points if you could receive a written transcript afterward.
ChatGPT’s Deep Research tool already offers something powerful: the ability to explore complex topics with clarity, speed, and structure. But it has room to grow. These eight feature suggestions aren’t just nice-to-haves—they’re logical next steps that could make the tool significantly more user-friendly, personalized, and professional. By giving users more control over how research is conducted, displayed, and accessed, OpenAI has a chance to take Deep Research from a great feature to an industry-leading one.
How to make an AI chatbot step-by-step in this simple guide. Understand the basics of creating an AI chatbot and how it can revolutionize your business.
Discover how Chrome's new AI-powered features are transforming the browsing experience. Learn about AI tools that enhance productivity and creativity.
A versatile AI platform offering innovative, configurable solutions to enhance user performance and adaptability in technology-driven environments.
Offer writing services, engage in email affiliate marketing, or consider social media management to make money online using ChatGPT.
Explore the differences between traditional AI and generative AI, their characteristics, uses, and which one is better suited for your needs.
Learn how to ensure ChatGPT stays unbiased by using specific prompts, roleplay, and smart customization tricks.
Mastering pricing strategies with AI helps businesses make smarter, real-time decisions. Learn how AI-powered pricing drives profits and sharpens your competitive edge.
Get 10 easy ChatGPT projects to simplify AI learning. Boost skills in automation, writing, coding, and more with this cheat sheet.
Discover four major reasons AI writing checkers flag human content and learn how to reduce false positives in your work.
Struggling to come up with a book idea? Find your next best-seller with ChatGPT by generating fresh concepts, structuring your book, enhancing writing, and optimizing marketing strategies.
Crack the viral content code with ChatGPT by using emotion, timing, and structure to boost engagement. Learn the AI techniques behind content that spreads fast.
Learn how AI apps like Duolingo make language learning smarter with personalized lessons, feedback, and more.
Insight into the strategic partnership between Hugging Face and FriendliAI, aimed at streamlining AI model deployment on the Hub for enhanced efficiency and user experience.
Deploy and fine-tune DeepSeek models on AWS using EC2, S3, and Hugging Face tools. This comprehensive guide walks you through setting up, training, and scaling DeepSeek models efficiently in the cloud.
Explore the next-generation language models, T5, DeBERTa, and GPT-3, that serve as true alternatives to BERT. Get insights into the future of natural language processing.
Explore the impact of the EU AI Act on open source developers, their responsibilities and the changes they need to implement in their future projects.
Exploring the power of integrating Hugging Face and PyCharm in model training, dataset management, and debugging for machine learning projects with transformers.
Learn how to train static embedding models up to 400x faster using Sentence Transformers. Explore how contrastive learning and smart sampling techniques can accelerate embedding generation and improve accuracy.
Discover how SmolVLM is revolutionizing AI with its compact 250M and 500M vision-language models. Experience strong performance without the need for hefty compute power.
Discover CFM’s innovative approach to fine-tuning small AI models using insights from large language models (LLMs). A case study in improving speed, accuracy, and cost-efficiency in AI optimization.
Discover the transformative influence of AI-powered TL;DR tools on how we manage, summarize, and digest information faster and more efficiently.
Explore how the integration of vision transforms SmolAgents from mere scripted tools to adaptable systems that interact with real-world environments intelligently.
Explore the lightweight yet powerful SmolVLM, a distinctive vision-language model built for real-world applications. Uncover how it balances exceptional performance with efficiency.
Delve into smolagents, a streamlined Python library that simplifies AI agent creation. Understand how it aids developers in constructing intelligent, modular systems with minimal setup.