Streamlining Deep Research Reports: Nate Herk's AI Automation Breakdown
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In this riveting episode of Nate Herk's AI Automation extravaganza, our hero embarks on a thrilling journey of workflow testing for a deep research report on the controversial topic of sugar for breakfast. With the click of a button, a form pops up, prompting the entry of the search topic and email for report submission. The workflow kicks off with a planning topic agent, which ingeniously generates five in-depth search topics and assigns them to various research agents for chapter creation. Each chapter delves into different aspects of the sugar debate, meticulously crafted by the team of AI assistants.
As the chapters take shape, the data is seamlessly transferred to Google Sheets for safekeeping and future reference. The team meticulously finalizes the content, compiling all sources into a numbered list for the report's source section. A table of contents is then generated based on the chapters and subjects within, leading to the seamless creation of a comprehensive 40-page PDF report. The sources are neatly organized and hyperlinked for easy access, showcasing the team's dedication to delivering a top-notch research experience.
In a jaw-dropping comparison, Nate Herk highlights the efficiency and cost-effectiveness of his workflow against competitors like ChatGBT, emphasizing the unlimited potential for running the process multiple times at a fraction of the cost. The team's strategic use of API calls from Tavi and api template.io ensures a smooth transition from data gathering to PDF generation, all while keeping a keen eye on the budget. With a live demonstration of a new research topic on the importance of sleep for young adults, Nate Herk showcases the power and versatility of his AI automation system, leaving viewers in awe of the endless possibilities in the world of deep research automation.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch I Built Deep Research That Beats Perplexity & OpenAI (free template) n8n tutorial on Youtube
Viewer Reactions for I Built Deep Research That Beats Perplexity & OpenAI (free template) n8n tutorial
Suggestion to use open AI image generation for visual richness in documents
Appreciation for the video series and learning from it
Positive feedback on the workflow
Mention of not paywalling the content and crediting the creator
Interest in decreasing costs with Google Gemini 2.5
Concern about paid APIs/SAAS
Question about optimizing the workflow with iterations
Idea of agents acting as quality control checkpoint
Excitement about learning n8n and replicating the workflow
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