What are some examples of effective prompts or queries to use with DeepResearch for complex tasks?

Effective prompts for DeepResearch should be specific, structured, and iterative to handle complex tasks. Start by breaking tasks into smaller subtasks, provide clear context, and use constraints to guide output. For example, instead of asking, “How do I optimize a machine learning model?” refine the query to focus on specific aspects like hyperparameter tuning, dataset preprocessing, or hardware acceleration. This approach reduces ambiguity and ensures the response aligns with your technical needs. Developers benefit from prompts that specify the problem domain, tools, and desired outcome, such as, “Compare the performance trade-offs between PyTorch’s DataLoader and TensorFlow’s tf.data for processing large image datasets on a GPU cluster.”

Including context and constraints is critical. For instance, when querying code-related tasks, specify the programming language, libraries, and environmental factors. A prompt like, “Generate a Python function using NumPy to calculate the F1 score for a multi-class classification problem, ensuring it handles imbalanced classes and avoids division-by-zero errors,” provides clear guardrails. Similarly, for system design tasks, include scalability requirements: “Design a distributed caching system for a read-heavy web app with 10M daily users, using Redis and AWS ElastiCache. Highlight latency reduction strategies and failure recovery mechanisms.” These details help DeepResearch prioritize relevant technical considerations.

Iterative refinement improves results. Begin with a broad query to scope the problem, then follow up with targeted prompts. For example, start with, “Explain the CAP theorem’s implications for distributed databases,” then drill down with, “How does Amazon DynamoDB handle consistency and availability trade-offs in practice?” If the initial response misses nuances, adjust the prompt: “Revise the DynamoDB example to include how read/write quorums affect latency in global deployments.” This back-and-forth mimics collaborative problem-solving, allowing developers to explore edge cases or validate assumptions. By structuring prompts as progressive dialogs, you can tackle complex tasks systematically while maintaining technical depth.