When a DeepResearch report is too broad, start by tightening the query’s focus. Add specific keywords or phrases that align with the precise information you need. For example, if searching for "machine learning in healthcare" returns overly general results, refine it to "machine learning for early detection of diabetic retinopathy in retinal imaging datasets." This adds context (medical imaging), a use case (early detection), and a data type (retinal datasets), which narrows scope. Use filters like date ranges, geographic regions, or content types (e.g., peer-reviewed studies vs. whitepapers) to exclude irrelevant material. Boolean operators like AND or NOT can further exclude tangential topics—e.g., "cloud security" AND "zero-trust architecture" NOT "hybrid cloud" avoids broader hybrid infrastructure discussions. If the tool supports advanced syntax (e.g., quotation marks for exact phrases), use it to reduce ambiguity.
If the report is too narrow, expand the query by removing overly restrictive terms. For instance, a search like "optimizing React component re-renders using React.memo in Next.js 13" might exclude broader optimization strategies. Simplify it to "React performance optimization techniques" and add related terms like useMemo, virtualization, or state management. Use OR operators to include synonyms or alternative frameworks—e.g., ("React" OR "Vue") AND "rendering performance". Adjust filters to include a wider date range or more source types (e.g., blogs, forums, or conference talks). Leverage DeepResearch’s topic clustering or related-term suggestions if available—these features often surface adjacent concepts you might have overlooked. For example, a narrow query about "GraphQL error handling" could expand to include "GraphQL middleware" or "schema validation" based on tool recommendations.
Iterate and validate. After adjusting the query, review the first few results to gauge relevance. If they’re still off-target, refine further. For overly broad results, prioritize specificity: add domain-specific jargon (e.g., "convolutional neural networks" instead of "AI models") or use the tool’s advanced settings to weight certain keywords higher. For overly narrow results, test broader categories—e.g., replace "Node.js serverless cold starts" with "serverless latency mitigation." If DeepResearch offers AI-driven query refinement (e.g., autocomplete or suggested filters), use it to discover terms you may not have considered. Always document changes to the query structure to identify what worked—this creates a repeatable process for future searches. For example, track how adding a filter for "case studies" or removing a date constraint affected result quality.