Answer the quality control system
Output reliability is ensured by a four-fold protection mechanism:
- Input Filtering: Add a "Content Moderation" node after the first Text Input node to return preset alerts for violations. It is recommended to export the list of customized blocking words and update it regularly.
- Intellectual constraints: Enable the "Strict Context" mode of the LLM node to force the model to answer only on the basis of the contents of the connected vector database to avoid hallucinations. The testing phase is validated with the "Answer Precision" test set.
- output calibrationAdd a "Validation" node before the final output, and set rules such as: maximum length, forbidden URLs, must contain keywords, etc. For customer service scenarios, it is recommended to enable the confidence threshold (confidence ≥ 0.65).
- artificial bottoming out: Add a "Human Review" branch node, which will transfer high-risk operations (e.g., order inquiries) or low-confidence answers to manual review.
Continuous optimization: Collect bad cases through the "Incorrect Answers" dashboard under Monitoring, and iterate the prompt project on a monthly basis (prompt templates can be found in Templates→Safety).
This answer comes from the articleLamatic.ai: a hosted platform for rapidly building and deploying AI intelligencesThe
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