How does DeepResearch balance breadth vs depth when researching a topic (i.e., covering many sources vs going deep into a few)?

DeepResearch balances breadth and depth by using a staged approach that prioritizes initial broad exploration followed by targeted deep dives. The process starts with a wide scan of available resources to identify key themes, trends, and high-impact sources. For example, when researching a new machine learning framework, the team might begin by aggregating documentation, forums, academic papers, and competitor tools to map the landscape. This phase uses automated tools (like web scrapers or API-based data collection) to efficiently gather a large volume of data. The goal is to avoid premature focus on narrow details while building a foundation of context.

Once the broad scan is complete, DeepResearch shifts to depth by filtering and prioritizing sources. Criteria like credibility (e.g., peer-reviewed papers vs. blog posts), relevance to the project’s goals, and frequency of citation help identify which topics warrant deeper analysis. For instance, if the initial scan reveals a specific optimization technique mentioned across multiple credible sources, the team might analyze its implementation details, performance benchmarks, and edge cases. To maintain efficiency, they often use tools like Python scripts for data parsing or visualization libraries (Matplotlib, Plotly) to highlight patterns in the aggregated data, ensuring depth is applied only to high-value areas.

The balance is maintained dynamically through iterative checks. If a deep dive uncovers unexpected complexity or gaps, the team might expand the breadth again to explore related topics. Conversely, if the initial scan shows redundancy in certain areas (e.g., ten nearly identical tutorials on a basic feature), the team narrows focus early. For example, when researching cloud security practices, the team might start with general best practices but pivot to focus on a specific vulnerability if initial data shows it’s underdocumented. This flexibility, combined with clear criteria for prioritizing sources, allows DeepResearch to adapt to the unique demands of each project without sacrificing thoroughness or efficiency.