Fogsight Content Accuracy Assurance Program
The correctness of the generated content is ensured through the following multiple mechanisms:
- Preventive phase::
- Input optimization: use "concept + qualifier" format (e.g. "law of entropy increase - second law of thermodynamics")
- Model selection: prioritize LLMs that focus on factual accuracy such as Gemini 2.5
- Generation Control::
- Add validation directive: "Cite definitions from the authoritative textbook University Physics"
- Enable rigor mode (parameter: accuracy_level=high)
- Verification mechanisms::
- Automatic marking of uncertainty (shown as "to be validated" yellow)
- Built-in fact-checking functionality: compare pre-defined knowledge graphs
- error correction process::
- Bug fix command template: "Change 'electron orbitals' to 'electron cloud probability distribution' in frame 3"
- Build a knowledge base of common errors to avoid repeating them
- expert model::
- Upload reference PDF/PPT to strengthen the basis of generation
- Access to specialized databases (e.g. IEEE Xplore) for the latest research results
Final recommendation: AI-generated + manual review dual-insurance model is recommended for key teaching content.
This answer comes from the articleFogsight: AI tool for generating instructional animations with one clickThe
May not be reproduced without permission:AI productivity tools " How to avoid intellectual errors in AI-generated instructional animations?
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