How can you ensure that DeepResearch covers all necessary aspects of a topic in its output (do you need to break the query into parts)?

To ensure DeepResearch covers all necessary aspects of a topic, breaking the query into smaller, focused parts is critical. This approach allows systematic exploration of subtopics, reducing the risk of overlooking details. For example, if a developer asks, “How does authentication work in modern web apps?” the query could be split into components like encryption standards (e.g., HTTPS), token-based systems (e.g., JWT), OAuth flows, and session management. By addressing each part individually, the research process can verify that foundational concepts (like hashing passwords) and advanced topics (like OAuth2 scopes) are both covered. This decomposition also helps align the output with the user’s intent, whether they’re seeking a high-level overview or implementation specifics.

Once subtopics are identified, an iterative refinement process ensures depth and accuracy. For instance, when researching “scalable API design,” initial results might focus on REST principles. The system would then expand to include related areas like rate limiting, caching strategies, and load balancing. Cross-referencing multiple sources (documentation, tutorials, case studies) helps fill gaps—e.g., noting that GraphQL solves some REST limitations but introduces new complexities. Automated checks could flag incomplete sections, prompting deeper investigation into overlooked tools (e.g., gRPC for low-latency APIs) or edge cases (e.g., handling versioning in distributed systems). This step-by-step refinement ensures technical nuances aren’t missed.

Finally, validation through user feedback or peer review helps catch gaps. For example, a response about “deploying machine learning models” might initially focus on cloud services like AWS SageMaker. Developers testing the output could highlight missing on-premise solutions (e.g., Kubernetes with Kubeflow) or optimization techniques (model quantization). Integrating feedback loops—such as version-controlled drafts or community annotations—ensures the system adapts to real-world needs. This approach mirrors code review practices, where peer input identifies blind spots, ensuring outputs remain practical and comprehensive for technical audiences.