Yes, users can significantly improve DeepResearch's processing speed and accuracy by providing initial context, clear goals, and relevant reference links. Supplying this information upfront reduces the time needed to clarify ambiguous requests or search for foundational data, allowing the system to focus on analysis and synthesis instead of guesswork. For example, if you’re researching a technical topic like "machine learning optimization techniques," specifying whether you need comparisons of algorithms, implementation examples, or performance benchmarks lets the system prioritize the right resources and avoid irrelevant tangents.
Providing reference links or trusted sources is particularly effective. For instance, sharing a GitHub repository containing a specific implementation or linking to a research paper you want analyzed gives DeepResearch a direct starting point. This eliminates the need for the system to spend cycles verifying basic facts or sifting through low-quality sources. If your query relates to a niche topic—like a recent framework update or an unpublished study—including links ensures the system bases its analysis on the exact materials you care about. Even general pointers like "focus on IEEE journals" or "exclude blog posts older than 2022" streamline the research process by narrowing the scope.
Finally, structuring your query with clear sub-questions or bullet points helps the system parse and prioritize tasks efficiently. For example, instead of asking, "Explain quantum computing," a request like "1) Compare superconducting qubits vs. photonic approaches, 2) List current industry leaders in each field, 3) Highlight key technical challenges" allows parallel processing of distinct subtasks. Similarly, pre-defining terminology (e.g., "Assume I’m familiar with neural networks but not transformers") reduces backtracking. These steps mirror how a developer might optimize an API call by reducing unnecessary parameters—maximizing efficiency by minimizing ambiguity.