When initial DeepResearch query results miss the mark, refining the query requires systematic adjustments to scope, terminology, and structure. Start by analyzing why the results are off-target. Common issues include overly broad or vague keywords, incorrect assumptions about data sources, or mismatched terminology. For example, a query like “machine learning optimization” might return generic articles instead of specific techniques like “gradient descent hyperparameter tuning.” To fix this, narrow the focus by adding context, such as a framework (e.g., PyTorch) or a use case (e.g., “for image classification”). Use filters like date ranges, domains, or document types to exclude irrelevant content. If results are too technical, add qualifiers like “beginners” or “practical examples.”
Next, adjust the query’s structure to align with how data is organized in the target datasets. Many research tools rely on keyword matching or semantic analysis, so precise terminology matters. For instance, searching for “Python async performance issues” might yield better results than “Python speed problems with async.” Incorporate Boolean operators (AND, OR, NOT) to exclude unrelated terms. If the initial query for “cloud security best practices” returns AWS-specific content but you need Azure examples, revise it to “Azure cloud security best practices NOT AWS.” Additionally, test synonyms or related terms. A query like “container orchestration” could be expanded to “Kubernetes deployment patterns” or “Docker Swarm management” based on the initial results’ gaps.
Finally, validate adjustments iteratively. Run the revised query and manually check a subset of results to gauge relevance. If the first 10 results still include off-topic content, refine further. For example, if a search for “React state management libraries” returns Redux tutorials but misses newer tools like Zustand, add explicit version constraints (e.g., “2020-2024”) or mention alternatives explicitly. Leverage platform-specific syntax, such as quotation marks for exact phrases (e.g., “serverless architecture”) or wildcards for partial matches (e.g., “microservice* deployment”). Document changes systematically—track which keywords, filters, or operators improved results. If the dataset is niche, consider reaching out to maintainers to verify terminology conventions. This iterative, data-driven approach ensures queries evolve to match the underlying data structure and user intent.