Can you chain multiple DeepResearch queries together to explore different angles or subtopics of a larger topic?

Yes, you can chain multiple DeepResearch queries to explore complex topics by systematically breaking them into subtopics and iterating on results. This approach works by treating each query as a step that builds on prior findings, allowing you to drill deeper into specific areas. For example, if you’re investigating a broad technical topic like “machine learning deployment challenges,” your first query might surface high-level issues like scalability or model drift. Subsequent queries could then target each subtopic (e.g., “monitoring strategies for model drift” or “scaling TensorFlow models in Kubernetes”) to gather detailed insights. This method ensures coverage of the topic from multiple angles while maintaining focus.

A practical example involves researching a topic like “blockchain scalability.” An initial query might highlight solutions such as sharding or layer-2 protocols. A follow-up query could focus on “sharding implementation trade-offs in Ethereum,” revealing technical hurdles like cross-shard communication. A third query might explore “layer-2 solutions comparison (Optimism vs. StarkWare),” providing performance benchmarks. By structuring queries this way, you avoid information overload and create a logical flow. Tools like scripted API calls or workflow automation (e.g., Python scripts chaining HTTP requests) can streamline this process, passing parameters from one query’s results to the next.

Developers implementing this strategy should start by outlining the core topic and identifying key subtopics using initial exploratory queries. For instance, when researching “cloud cost optimization,” you might first identify common pain points (e.g., idle resources, data transfer fees). Next, use those findings to craft targeted queries like “automated shutdown of idle EC2 instances using AWS Lambda” or “reducing S3 cross-region transfer costs.” Tools like Jupyter Notebooks or workflow engines (Apache Airflow) can help organize and automate query sequences. However, challenges include ensuring result relevance (e.g., filtering outdated articles) and managing dependencies between queries. By validating intermediate outputs and refining search parameters at each step, you maintain accuracy and depth across the research chain.