Yes, DeepResearch can be directed to focus on specific subtopics or questions within a broader research topic. This is achieved by structuring inputs, constraints, and feedback mechanisms to guide the system’s analysis. For example, if you’re researching a broad area like "machine learning in healthcare," you could narrow the scope to "privacy-preserving techniques for medical image analysis." By providing explicit subtopics, keywords, or questions (e.g., "Compare federated learning vs. differential privacy in MRI datasets"), the system prioritizes relevant data sources, filters out unrelated information, and structures outputs around the specified focus. This ensures the research process aligns with the user’s goals while retaining flexibility to explore connections within the narrowed scope.
To implement this, DeepResearch typically uses a combination of user-defined parameters and iterative refinement. For instance, developers might configure the system via API calls or UI settings to include/exclude specific terms, prioritize certain data types (e.g., academic papers vs. industry reports), or limit timeframes. Additionally, interactive feedback loops—like rating the relevance of initial results—allow the system to adapt its focus dynamically. A practical example: if researching "blockchain scalability," you might first exclude topics like "cryptocurrency regulation" and then iteratively refine the search to emphasize layer-2 solutions (e.g., rollups) based on initial findings. This balances automation with user control, ensuring the output remains targeted.
The effectiveness of this approach depends on clear scoping and technical integration. For example, a developer studying "cloud security" could use DeepResearch to compare zero-trust architectures across AWS, Azure, and GCP by specifying sub-queries like "IAM role vulnerabilities" or "encryption key management." The system might then synthesize data from documentation, vulnerability databases, and case studies while ignoring broader topics like general cloud cost optimization. Tools like custom classifiers or topic modeling algorithms can further automate subtopic identification. However, overly narrow constraints risk missing relevant insights, so balancing specificity with exploratory capacity is key—a challenge familiar to developers designing filtered search systems or recommendation engines.