Yes, users can significantly improve DeepResearch’s processing speed and accuracy by providing structured initial context and relevant reference links. This approach helps the system focus on the most critical information, reduces ambiguity, and minimizes time spent on unnecessary data exploration. By giving the system a clear starting point, users enable it to prioritize tasks, allocate resources efficiently, and avoid tangential analysis.
First, providing structured context upfront is key. For example, if you’re asking for an analysis of a software bug, explicitly stating the programming language, framework versions, error logs, and steps to reproduce the issue allows the system to skip generic troubleshooting steps. Instead of a vague query like “My app crashes,” a detailed input such as “React Native 0.72 app crashes on iOS when accessing camera via expo-camera 13.5.0, error code E_CAMERA_UNAVAILABLE” lets the system immediately narrow down to platform-specific documentation, version compatibility tables, or known GitHub issues. Similarly, organizing context into bullet points or numbered lists helps the system parse relationships between data points faster than unstructured paragraphs.
Second, including reference links to documentation, code repositories, or research papers gives DeepResearch direct access to authoritative sources. For instance, linking to a GitHub repo with a specific file (e.g., https://github.com/user/project/blob/main/src/api.js#L45-L60) allows the system to analyze the exact code snippet in question without crawling through the entire project. If you’re asking about a machine learning concept, linking to a research paper or a PyTorch documentation page ensures the system bases its analysis on the correct methodology. Avoid vague references like “the official docs” – instead, provide URLs to specific sections (e.g., TensorFlow’s gradient checkpointing guide). This reduces latency caused by searching or inferring source material.
Finally, breaking complex queries into smaller, logically ordered sub-questions helps DeepResearch process tasks incrementally. For example, instead of asking “How do I optimize my distributed system?” split it into steps: “1. Identify bottlenecks in Redis cluster with 10K QPS, 2. Compare connection pooling strategies in Java clients, 3. Analyze tradeoffs between synchronous vs. asynchronous replication.” This allows the system to parallelize subtasks, cache intermediate results, and reuse computations across steps. Additionally, specifying output formats (e.g., “Return a JSON schema for the API response” or “Generate a table comparing AWS Lambda vs. Google Cloud Functions”) reduces post-processing time by aligning the result structure with your needs upfront.