Background to the issue
AI-generated content often suffers from logical breaks or lack of creativity. massGen's iterative optimization mechanism systematically improves output quality.
prescription
- Multiple iterations:configure
max_iterations: 5Trigger the deep optimization process
Every round:
1. Generation of drafts
2. Cross-reviews
3. Integration improvements - Division of roles:pass (a bill or inspection etc)
--agent-rolesDesignation:
- Lead author (responsible for core content)
- Editing (checking for coherence)
- Fact-checker - Quality Anchors:Provide sample paragraphs in the prompt (e.g., an award-winning press release) using the
--reference examples/news_sample.md
Practical Cases
Combined use when generating technical white papers:
- Gemini Processing Data Visualization Description
- GPT-4 is responsible for industry insights
- Claude audits for technical accuracy
The final 90% content overlap rate was reached through 3 rounds of iterations.
This answer comes from the articleMassGen: A Multi-Intelligence Collaborative Task Processing SystemThe
May not be reproduced without permission:AI productivity tools " How to optimize the quality of content generation in MassGen?
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