DeepResearch-generated reports typically range between 10-15 pages (3,000-5,000 words) when covering a standard technical or business topic. These reports aim to balance depth and readability, including sections like an executive summary, methodology, analysis, and conclusions. For example, a report on "AI Model Optimization Techniques" might include comparisons of methods, code snippets, performance benchmarks, and implementation trade-offs. This structure ensures clarity for developers while providing actionable insights.
The length and detail of reports can be adjusted through user-defined parameters. For instance, you can specify the scope (e.g., "focus only on quantization methods"), set a page or word limit, or adjust the technical depth (e.g., "include code examples" vs. "high-level overview"). Some tools also let users prioritize sections—like reducing background explanations for expert audiences. APIs or configuration files often allow granular control, such as limiting subsections or excluding appendices. This flexibility ensures reports align with specific use cases, like a 2-page summary for a sprint review versus a 20-page document for architectural planning.
Customization also depends on input quality. Providing detailed prompts, targeted keywords, or example templates helps DeepResearch narrow its focus. For example, specifying "compare PyTorch and TensorFlow for edge device deployment, include latency metrics and memory usage graphs" yields a more concise, data-driven report than a generic query. Some platforms offer preset templates (e.g., "academic," "technical brief," "presentation slides") to standardize outputs. However, overly restrictive constraints (e.g., "explain Kubernetes networking in 500 words") might lead to omitted details, requiring a balance between brevity and completeness.