Yes, you can chain multiple DeepResearch queries to explore different angles or subtopics of a larger topic. This approach works by iteratively refining or expanding your search based on insights from prior results. Each query builds on the previous one, allowing you to drill deeper into specific areas, uncover connections between subtopics, or address gaps in your initial findings. For example, if your main topic is "machine learning security," your first query might focus on general vulnerabilities, a second could explore adversarial attacks, and a third might investigate mitigation techniques like robust model training.
To chain queries effectively, start with a broad search to establish foundational knowledge. Analyze the results to identify recurring themes, unanswered questions, or underdeveloped areas. For instance, if initial research highlights "model poisoning" as a key vulnerability, your next query could target specific attack vectors (e.g., data manipulation during training). Tools like keyword extraction from results or citation tracking can help pinpoint terms or papers to refine subsequent searches. You might also use filters (e.g., publication date, domain-specific sources) to narrow or broaden scope as needed. This method mirrors how developers iterate through code debugging—using feedback from each step to guide the next.
However, chaining queries requires careful planning to avoid redundancy or scope creep. Define clear objectives for each step: Are you exploring cause-effect relationships, comparing solutions, or validating claims? For example, after researching "privacy in federated learning," a follow-up query could compare differential privacy techniques across frameworks like TensorFlow Federated or PySyft. Documenting intermediate findings ensures continuity and helps avoid circular reasoning. While automated tools can assist (e.g., scripting API calls to chain searches), manual oversight is often necessary to maintain focus and interpret context-specific nuances. This structured yet flexible approach enables thorough exploration without sacrificing depth.