To get the most relevant results from DeepResearch, focus on clarity, specificity, and context. Start by clearly defining the problem or topic you’re exploring. Avoid vague terms and instead use precise keywords that align with the technical domain you’re targeting. For example, instead of asking, “How do I optimize code?” specify the language, framework, and use case: “What are strategies to reduce latency in a Python Flask API handling 10k concurrent requests?” This helps the tool prioritize technical depth over generic advice.
Structure your query to include constraints or requirements. If you’re troubleshooting an error, provide the exact error message, environment details (e.g., “Node.js 18 on AWS Lambda”), and steps you’ve already tried. For example, “How to resolve ‘ECONNRESET’ errors in a Node.js server using Express and PostgreSQL, after TLS configuration changes?” This narrows the scope and reduces irrelevant suggestions. If you’re comparing tools or approaches, specify criteria like scalability, cost, or compatibility: “What’s the most efficient vector database for real-time semantic search with 1M+ embeddings, using Python?”
Include context about what you already know and where you’re stuck. For instance, “I’ve implemented JWT authentication in my React app but need to handle token refresh securely without exposing it to XSS.” This prevents redundant explanations and directs the tool to fill knowledge gaps. Avoid open-ended questions like “Explain microservices” unless you need a high-level overview. Instead, tie it to a practical scenario: “How to handle distributed transactions across microservices using Saga pattern in Java with Spring Boot?” This balances specificity with the flexibility to explore nuanced solutions.