If you suspect DeepResearch misunderstood your query or the scope of your topic, start by reviewing and refining your input. First, verify that your query is specific, unambiguous, and free of vague terminology. For example, if you asked for "best practices for scaling systems," clarify whether you’re referring to horizontal scaling, vertical scaling, or a specific technology like Kubernetes. Rephrase the question to include context, constraints, or domain-specific terms (e.g., "What are proven strategies for horizontal scaling of microservices in AWS?"). This reduces ambiguity and aligns the response with your actual needs.
Next, break down complex questions into smaller, focused parts. If the initial response misses the mark, iterate by addressing gaps in the output. For instance, if you asked about "optimizing database performance" and received generic advice, follow up with targeted sub-questions like, "How do I reduce query latency in PostgreSQL for read-heavy workloads?" or "What indexing strategies improve write performance in time-series data?" Providing examples of what you’ve already tried or specific pain points (e.g., slow joins, lock contention) can also help DeepResearch narrow its focus and deliver actionable insights.
Finally, validate the output against trusted sources or domain knowledge. If the response seems inconsistent with established practices, test it against documentation, forums, or peer-reviewed resources. For example, if DeepResearch suggests an unconventional approach to handling API rate limits, cross-check it with official provider guidelines or community discussions. If discrepancies persist, consider rephrasing the query using alternative terminology or explicitly defining the scope (e.g., "Exclude cloud-based solutions" or "Focus on Python libraries"). Iterative refinement and explicit constraints ensure the output aligns with your technical requirements.