Research

To conduct multi-source research, you can launch parallel queries across Perplexity, Gemini, and Claude. The system decomposes your question into focused sub-queries and synthesizes the findings with source attribution and confidence levels.

Getting diverse perspectives on a complex topic is best achieved through parallel multi-source research. By orchestrating multiple AI models to fetch information simultaneously, you synthesize findings with source attribution to ensure comprehensive coverage.

You can generate a structured JSON report by running parallel searches across multiple sources. The research process synthesizes the fetched data into a structured output format, including a dedicated research metrics section and source attribution.

Yes, you can adjust the depth of multi-source research investigations using Quick, Standard, and Extensive modes. Each mode defines a specific workload and output format, allowing you to scale the research process based on your needs.

Yes, parallel research automatically decomposes broad user questions into focused sub-queries. This ensures coverage of core facts, context, and implications before executing parallel queries in a single operation for faster results.

You should use extensive research mode over a quick search when you need a higher workload and more detailed output format for complex topics. Quick mode is suitable for faster, less comprehensive multi-source investigations.