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To generate a KOICA evaluation report without AI hallucination, use an evidence-gated workflow that synthesizes project data and strictly prohibits unsupported claims. This enforces factual accuracy by requiring source materials for every drafted narrative.

You can check numerical consistency in project evaluation reports using automated code-based validation. This process verifies numerical alignment across different languages and tables to prevent common reporting errors.

Citation verification against internal regulations is a multi-stage pipeline stage that validates drafted report citations. It ensures project completion reports adhere to strict evaluation guidelines by checking factual references.

Yes, this report generation workflow requires Python to operate its multi-stage pipeline. The environment uses Python to execute automated numerical consistency checks and validation scripts.

The best way to automate narrative hallucination detection in evaluation reports is through a dedicated narrative-verifier agent. This agent reviews drafted text to detect fabricated statements and ensure professional quality.

A limitation of using automated agents for project completion reports is the strict dependency on existing evaluation results and project source data. The agents cannot draft chapters or generate claims without integrated source materials.