5 Mind Blowing Use Cases of Deep Research | Highlights and Annotations by Gistr.

This video reviews AI deep research capabilities in Perplexity, Gemini, ChatGPT, and Jens Spark. The author finds ChatGPT's deep research most comprehensive but slowest, while Perplexity is fastest but least detailed. Jens Spark is highlighted as a free, strong alternative. The video details how to use deep research for business, including tool research, client proposals, target audience analysis, regulatory compliance, and market opportunity identification. A four-part prompting framework is provided to maximize results. After spending over 50 hours testing deep research across multiple AI models, the presenter will share their findings on what works and what's a waste of time, providing a breakdown of each platform's capabilities without marketing hype and real-world examples of solving business problems. This establishes the credibility of the upcoming analysis. AI companies are hyping a "deep research" mode across various models (Perplexity, Gemini, ChatGPT), but the presenter will later introduce a free alternative they believe surpasses these well-known options. This segment highlights the current market trend and sets the stage for a comparative analysis. A detailed analysis of Perplexity's deep research capabilities highlights its speed (under 3 minutes), wide range of sources, and clear reports with citations. However, it also points out weaknesses like poor contextual memory in long conversations, making it the presenter's least favorite option. This offers a balanced perspective on a specific tool.This segment compares Gemini and ChatGPT's deep research modes. Gemini is described as methodical, using reputable sources and integrating well with Google Drive, but potentially lacking diverse perspectives. ChatGPT offers the most comprehensive capabilities but has the slowest processing time and struggles with source reliability. This provides a comparative analysis of two major players.The presenter discusses ChatGPT's deep research capabilities, emphasizing its comprehensive research, detailed reports, and ability to analyze files and images. However, it also highlights the high cost ($200/month for full access) and limitations on the cheaper plan. This segment focuses on the practical considerations of using ChatGPT's deep research.A side-by-side comparison of the same prompt across Perplexity, Gemini, and ChatGPT deep research models demonstrates the varying outputs and strengths of each. The presenter highlights the differences in the number of sources, depth of analysis, and overall report quality. This offers a visual and practical comparison of the different models.The presenter introduces Jens Spark, their preferred free deep research platform, showcasing its output and unique features like mind maps and a detailed table of contents. The "mixture of agents" functionality, which automatically selects the best AI models for the task, is highlighted. This introduces a strong contender and a key finding of the video.The presenter provides two unconventional business use cases for deep research: researching new tools for the business and conducting client research for proposals. The benefits of using deep research for these tasks, such as saving time and making more informed decisions, are emphasized. This provides practical applications of the technology.The third unconventional use case focuses on conducting in-depth target audience research to uncover behavioral patterns and preference shifts, providing actionable insights for marketing and product development. This demonstrates the application of deep research for market understanding.The fourth and fifth use cases cover regulatory compliance research and market opportunity analysis, highlighting how deep research can save time and money while identifying emerging trends. This completes the practical application examples. The presenter discusses the ROI of using deep research, demonstrating how the time saved significantly outweighs the subscription costs. They also reiterate the value for various audiences, including business owners, entrepreneurs, and consultants. This summarizes the financial and practical benefits.The presenter clarifies who would benefit most from deep research, targeting professionals in intensive knowledge work and those needing quick, informed decisions. This segment clarifies the target audience for the technology. A side-by-side comparison of a standard prompt and a deep research prompt in ChatGPT showcases the significant difference in output quality. The deep research prompt yields detailed, actionable intelligence with current data and citations, highlighting the power of effective prompting. This demonstrates the core benefit of the deep research feature.The presenter explains the mechanics of deep research, describing it as a personal research assistant that actively searches the web, synthesizes information, and generates detailed reports. The three-step process (collecting sources, reasoning, and outputting reports) is explained, emphasizing the importance of understanding each platform's approach. This provides a foundational understanding of the technology. The presenter introduces a four-part deep research prompting framework that consistently delivers superior results, emphasizing the importance of effective prompting techniques. This provides a practical methodology for utilizing the technology.This segment details the crucial first step in effective deep research prompting: being extremely specific about your research needs, including the required depth, specific sources (inclusion/exclusion), and relevant time periods. This precise specification ensures the AI understands the query's scope and focuses its efforts accordingly.This segment emphasizes the importance of specifying the desired output format in your prompt. It advises detailing the report structure, required sections, data visualization preferences, and citation style to ensure the AI delivers the information in the user's preferred manner.This section highlights the critical, often-missed step of instructing the AI on how to handle conflicting information. It explains the need to provide prioritization criteria for sources, guidelines for resolving conflicts, and defining sufficient evidence for conclusions, ultimately ensuring the AI's reasoning process aligns with the user's expectations.This segment stresses the importance of providing context for the research, including the decision-making context, problems to be solved, planned actions based on findings, and the user's level of expertise. This contextual information helps the AI tailor its output to the user's specific needs and knowledge level. This segment compares various AI platforms suitable for deep research, highlighting Perplexity's speed, Gemini's methodical approach, CHBT's depth (with budget considerations), and JpARK-AI's potential as a balanced option. It emphasizes that regardless of the chosen platform, effective prompting remains the key to unlocking the full potential of deep research AI models. AI deep research tools (ChatGPT, PerplexityAI, Gemini) offer varying capabilities beyond standard search. Deep research excels at in-depth analysis, generating detailed reports with citations, unlike basic web searches. Each platform's deep research mode has strengths and weaknesses: PerplexityAI is fast; Gemini is methodical and reliable; ChatGPT is comprehensive but slower. A four-part prompting framework is crucial for effective deep research: defining scope, specifying output format, directing reasoning, and clarifying context. Deep research is valuable for various applications: researching new tools, conducting client research, analyzing target audiences, and creating content. Proper prompting unlocks deep research's full potential, yielding superior results compared to standard prompting techniques. Deep research saves time and resources, especially for intensive knowledge work, offering significant cost savings. The choice of platform depends on specific needs: speed (PerplexityAI), reliability (Gemini), or comprehensive depth (ChatGPT). Deep research empowers businesses, entrepreneurs, consultants, and content creators with data-backed insights and well-researched content. Here are the key differences noted between the deep research capabilities: All three platforms (Perplexity, Gemini, ChatGPT) offer a "Deep Research" mode, aiming to provide more depth than standard AI answers which can be shallow, outdated, or incorrect . ChatGPT's deep research report was observed to be very brief and significantly less in-depth compared to the others . ChatGPT does provide a table format in its report and allows for follow-up questions, including suggested ones, similar to Perplexity . Gemini was found to be particularly effective at identifying emerging market trends by analyzing a wide range of data sources, providing strong output for topics like market opportunities and challenges . Based on the speaker's description of a deep research prompting framework, here are the key steps mentioned: Define the Scope: Start by clearly outlining the boundaries of your research question. Be very specific about what information you need, the required depth of research, any particular sources to include or exclude, and relevant time periods . Specify the Output Format: Indicate how you want the research presented. This includes details like required sections (especially if for a specific purpose or company), data visualization preferences, and desired citation styles . Direct AI Reasoning: Crucially, instruct the AI on how it should handle and reason through conflicting information it might encounter during the research process .