deep-research

Multi-model deep research works by querying OpenAI, Perplexity, and Gemini simultaneously in parallel. It synthesizes the gathered findings into a comparative report, highlighting agreements, disagreements, and unique insights across all participating providers.

The best way to validate AI citations is using a Skill that includes citation validation as part of its synthesis process. It verifies the source quality and relevance of research findings automatically while generating your comparative analysis report.

Yes, you need API keys to run parallel queries with OpenAI, Perplexity, and Gemini. The Skill requires valid API keys for participating models to authenticate requests and execute the Python data retrieval and analysis scripts.

Yes, you can automate comparative analysis using Python scripts to retrieve data from multiple models. This Skill executes Python scripts to query providers in parallel and synthesizes findings into a report with provider performance metrics.

Limitations of multi-model deep research include dependency on external API keys for OpenAI, Perplexity, and Gemini. It is also constrained by the execution environment needed to run Python scripts for data retrieval and citation validation.