How can users effectively simplify or break down a query to fit within DeepResearch's capabilities if needed?

To effectively simplify or break down a query for DeepResearch, users should focus on isolating the core components of their problem and structuring the query in a way that aligns with the tool’s processing capabilities. Start by identifying the primary goal of the query and removing unnecessary context or tangential details. For example, if a user wants to analyze the performance of a machine learning model, they might split the query into sub-questions about data preprocessing, model architecture, and training parameters instead of asking for a single broad analysis. This decomposition reduces complexity and allows DeepResearch to address each component systematically.

Next, structure the query with clear intent and explicit boundaries. Use bullet points, numbered lists, or explicit sub-questions to separate distinct aspects of the problem. For instance, instead of asking, “How do I optimize my database for high traffic while ensuring security?” break it into: “1. What indexing strategies improve read performance under high traffic? 2. How can I implement role-based access control without impacting query speed?” This approach ensures each sub-question is specific and reduces ambiguity. Developers should also avoid open-ended phrasing (e.g., “What’s the best way to…?”) and instead specify constraints, such as programming languages, frameworks, or performance metrics, to narrow the scope.

Finally, iterate and refine based on initial results. If a query returns incomplete or overly broad answers, users can adjust by adding granularity or rephrasing technical terms. For example, if a query about “API rate limiting” yields generic advice, follow up with specifics like, “What algorithms are suitable for rate limiting in a distributed Node.js backend?” This iterative process helps align the query with DeepResearch’s ability to parse and retrieve precise technical information. Developers should also test variations of their queries to identify which structures yield the most actionable insights, balancing specificity with the tool’s response patterns.