Effective prompts for DeepResearch should be specific, provide context, and outline clear goals to handle complex tasks. The key is to structure queries in a way that guides the tool to synthesize information, compare data, or analyze patterns. Below are three examples of effective prompts for different scenarios, along with explanations of why they work.
Example 1: Comparative Analysis Prompt: "Compare the performance, scalability, and cost-effectiveness of AWS Lambda vs. Google Cloud Functions for real-time data processing in Python. Focus on cold start times, concurrency limits, and integration with other cloud services (e.g., databases, messaging queues). Provide a summary table and recommendations for high-throughput applications." This works because it defines the scope (AWS vs. Google Cloud), specifies technical criteria (performance, scalability, cost), and requests actionable output (table + recommendations). By narrowing the focus to real-time Python workloads, the query avoids generic comparisons and ensures the response addresses practical developer concerns like cold starts and concurrency. The structured output (table) also makes it easier to digest technical details.
Example 2: Debugging/Troubleshooting Prompt: "Explain why a Python script using asyncio throws a 'RuntimeError: Event loop closed' when integrating with a REST API. List common causes, such as improper coroutine handling, thread conflicts, or resource cleanup issues. Provide code snippets to reproduce the error and step-by-step fixes." This prompt is effective because it targets a specific error scenario and asks for both root causes and solutions. By mentioning integration with a REST API, it adds context about the environment. Requesting code snippets helps developers validate the issue, while step-by-step fixes ensure actionable guidance. This structure mirrors how developers approach debugging—starting with symptoms, diagnosing causes, and applying fixes.
Example 3: Emerging Technology Research Prompt: "Summarize advancements in Rust-based blockchain frameworks over the past two years. Highlight key features like smart contract support, consensus mechanisms, and interoperability with Ethereum. Include examples like Polkadot, Solana, and Near Protocol, and evaluate their adoption in enterprise vs. decentralized applications." This query works because it defines a timeframe (past two years), specifies technical focus areas (smart contracts, consensus), and requests real-world examples. By contrasting enterprise vs. decentralized use cases, it ensures the analysis addresses practical adoption challenges. This approach avoids vague summaries and instead provides a framework for evaluating niche technologies, which is critical for developers assessing tools for specific projects.
In all cases, effective prompts avoid ambiguity, define evaluation criteria, and request structured outputs. They also align with common developer workflows—whether comparing tools, troubleshooting, or researching technologies—making the results immediately applicable to real-world tasks.