To effectively simplify a query for DeepResearch, start by breaking it into smaller, focused components. Complex questions often combine multiple sub-questions or require analysis across several domains. For example, instead of asking, "What are the economic, social, and environmental impacts of renewable energy adoption in Europe?" split it into parts: "What are the economic benefits of solar energy in Germany?" followed by "How has wind energy adoption affected rural communities in Spain?" This approach reduces ambiguity and allows the system to address each aspect systematically. Developers should treat each sub-question as a standalone task, iterating through them to build a comprehensive answer.
Next, prioritize specificity and clarity. Avoid vague terms or overly broad requests. For instance, a query like "Explain machine learning" is too general. Instead, narrow it to "Describe how gradient descent optimization works in neural networks" or "Compare supervised vs. unsupervised learning use cases in healthcare." Use concrete parameters, such as timeframes, technologies, or metrics, to define the scope. If a query involves comparisons, structure it as a list: "List three advantages of React over Angular for state management in large-scale apps." This reduces the cognitive load on the system and increases the likelihood of precise, actionable outputs.
Finally, simplify technical jargon or abstract concepts when possible. If a query includes niche terminology (e.g., "Explain homomorphic encryption in zero-trust architectures"), rephrase it using foundational terms: "How does encryption allow data processing without decryption in secure systems?" If the initial attempt fails, use an iterative approach: Start with a high-level question ("What is blockchain?") and progressively add layers ("How do consensus algorithms like Proof of Work function in blockchain networks?"). This allows the system to handle incremental complexity while ensuring alignment with its knowledge boundaries. Developers can also use explicit constraints, such as "Focus on Python examples" or "Limit the explanation to 500 words," to guide the output format.