Users often make three key mistakes when formulating queries for DeepResearch, leading to suboptimal results. First, overly broad or vague search terms reduce precision. Second, neglecting to structure queries for the system’s syntax limits effective filtering. Third, failing to account for domain-specific nuances can return irrelevant data. Addressing these issues improves result quality and relevance.
A common issue is using imprecise or generic terms. For example, searching for “machine learning” without additional context returns a vast, unfocused dataset. Instead, specifying “machine learning model compression techniques for edge devices 2020-2023” narrows the scope. Developers should avoid single-word queries and include modifiers like timeframes, use cases, or technical constraints. Another pitfall is omitting Boolean operators (AND, OR, NOT) or parentheses to group terms. A query like "AI security (adversarial attacks OR data poisoning)" ensures the system prioritizes either sub-topic within the broader context, whereas “AI security adversarial attacks data poisoning” might misinterpret relationships between terms.
Ignoring domain-specific syntax or filters is another mistake. DeepResearch often supports operators like "filetype:pdf", "author:", or “site:arxiv.org” to restrict searches. A query like “neural architecture search benchmarks” could miss key papers if it doesn’t include "filetype:pdf site:arxiv.org". Similarly, technical acronyms (e.g., “GANs” vs. “generative adversarial networks”) might not be consistently indexed. Developers should test variations and use standardized terminology. For instance, “transformer attention mechanisms in NLP” is clearer than "AI text models with attention", as the latter might surface non-technical content. Always verify the platform’s supported syntax and tailor queries to leverage filters, exact phrases (using quotes), and domain-specific jargon.