Building a multi-tiered fact-checking system
Open Deep Research has designed the following safeguard mechanisms to address the possible accuracy risks of AI-generated content:
- Cross-validation workflow: By default, the system will perform three rounds of validation (initial acquisition → questioning reflection → final confirmation) for each fact point, and the error rate is reduced by more than 60% compared to a single generation.
- credible source prioritization strategy: By configuring the `source_priority.yaml` file, you can set the weight of authoritative sources such as academic journals, government websites, etc., and the system will automatically prioritize the use of high-credibility sources.
- Metadata Traceability: Each assertion in the report is labeled with a specific source URL and timestamp to facilitate manual review.
- Confidence labeling system: At the end of the report, a "fact-checking table" is generated with confidence scores for each conclusion (based on number of sources and consistency).
Implementation Steps:
- Create `reliable_sources.txt` in the project root directory to list whitelisted websites
- Run the command to add validation parameters: `python main.py -topic "gene editing ethics" -verify_level strict`
- Check the original material in the generated `_sources/` directory
- Improving inference accuracy with higher performance models such as `-model=gpt-4`
Special recommendation: For high-risk areas such as healthcare and finance, make sure to enable human review mode (add `-human_review=true` parameter).
This answer comes from the articleTogether Open Deep Research: Generating Indexed Deep Research ReportsThe
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