Controls to ensure consistency of response
Chatly's unique model collaboration technology solution:
- Model Lock:Fixed primary AI engine in account settings (e.g., GPT-4 is used uniformly for all conversations)
- Contextual anchors:Using the [the following remains consistent] command, the system creates a memory marker
- Output Calibration:Click on key responses to request multiple version comparison and select the result that best meets expectations to train the AI
- Task triage:Intelligent assignment of models based on functional modules (creative content → GPT-4/fact-checking → PaLM 2)
Management Suggestions: 1) Establish independent dialog threads for different projects; 2) Record quality prompts to form a knowledge base; 3) Use clarification instructions when contradictory information is detected; 4) Export quality dialogs as standard reference answers on a regular basis. Technical Background: The platform bottom layer uses a consistent hash algorithm to ensure the stability of model allocation for the same topic.
This answer comes from the articleChatly: Intelligent Chat and Content Generation Tool with Integration of Multiple AI ModelsThe
May not be reproduced without permission:AI productivity tools " How to avoid response inconsistency problems in multi-model platforms?
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