When users submit queries to DeepResearch, common mistakes often stem from unclear or unstructured input. A primary issue is using overly broad or vague terms without context. For example, searching for “climate change” without specifying a timeframe, geographic region, or subtopic (e.g., “economic impacts” or “Arctic ice melt”) can return an overwhelming volume of irrelevant results. The system relies on precise keywords and modifiers to narrow down sources effectively. Without these, users may miss critical studies buried in generic results.
Another frequent error is neglecting to use advanced search syntax or filters. DeepResearch supports operators like quotes for exact phrases, Boolean terms (AND/OR/NOT), and field-specific filters (e.g., “author:Smith” or “year:>2020”). Users unfamiliar with these features might write a query like “renewable energy costs Germany” instead of structuring it as “renewable energy” AND (cost OR economics) AND “Germany” AND (year:2018-2023). The latter approach reduces ambiguity and ensures the system prioritizes recent, region-specific data. Similarly, failing to exclude irrelevant terms (e.g., “-solar” when researching wind energy) can pollute results with unrelated content.
Lastly, users often overlook iterative refinement. For instance, if a query like “AI ethics guidelines” returns too many policy documents, they might not adjust it to target technical standards or industry-specific frameworks. DeepResearch’s algorithms improve with feedback, so not using “exclude” filters or synonym variations (e.g., “governance” instead of “guidelines”) limits result quality. Additionally, typos or inconsistent terminology (e.g., “machine learning” vs. “ML”) can derail searches, as the system treats these as distinct terms unless explicitly instructed otherwise. Clear, structured queries with iterative adjustments yield the best outcomes.