DeepResearch generates comprehensive reports by combining multiple steps that go beyond surface-level query responses. Instead of treating a question as a single task, the system breaks it down into subtopics, gathers diverse data sources, and synthesizes the information into a structured format. This approach ensures depth and context, which a single answer might lack.
First, the tool analyzes the input query to identify core themes and related subquestions. For example, if asked about "the impact of AI on healthcare," it might decompose the topic into areas like diagnostic tools, ethical concerns, cost reduction, and patient outcomes. Each subtopic becomes a section in the report. To gather data, DeepResearch pulls information from databases, academic papers, industry reports, and real-time sources, using APIs or preprocessed datasets. This step ensures the report isn’t limited to a single perspective—for instance, combining clinical trial results with economic analyses and patient surveys.
Next, the system synthesizes the collected data. It cross-references sources to validate claims, resolves contradictions by prioritizing authoritative or recent data, and highlights trends or patterns. Advanced natural language processing (NLP) models summarize key points and generate coherent explanations. For technical audiences, this might involve translating jargon-heavy research into accessible explanations while preserving accuracy. Finally, the tool structures the content into sections (e.g., introduction, methodology, findings, conclusions) and formats it for readability, using visual aids like charts or tables where applicable. This layered process—decomposition, multi-source aggregation, synthesis, and structuring—enables DeepResearch to produce reports that address both breadth and depth, tailored to the user’s needs.