advisory-board

Multi-model consensus building is facilitated by convening an AI advisory board that orchestrates leading models to review, debate, and converge on a single recommendation. The process uses Python scripts to structure rounds of independent review and cross-examination.

Multi-model analysis is a decision support mechanism where multiple AI models independently evaluate a source material and cross-examine each other. It generates a detailed scorecard and consensus recommendation to support complex AI decision-making.

Yes, you need AI model APIs and Python3 libraries installed in your environment to orchestrate the advisory board reviews. These dependencies are required to manage the multi-model analysis, cross-reading summaries, and consensus building scripts.

You configure multi-model advisory board debates by running the orchestration script with arguments specifying the source material, sensitivity, number of review rounds, and cross-reading method. For example, use command line flags to set two rounds with summary cross-reading.

Yes, advisory board consensus building supports sensitivity settings to handle sensitive strategic planning documents safely. You can specify the sensitivity level as a command line argument when initiating the multi-model review and debate process.

The best way to evaluate diverse AI perspectives is to run a structured advisory board review that applies scoring criteria across multiple rounds of debate. This generates a detailed scorecard for analyzing the decision-making process and converging on a recommendation.