Three Optimization Strategies for Efficiency Improvement
The following approach is recommended for CommonGround workflow efficiency issues:
- parallelization: To transform a linear workflow into a parallel branch, enter edit mode and use the "Parallel Block" function to wrap groups of nodes that can be executed at the same time.
- caching mechanism: Enable caching of the results of repeated calls to intelligences by setting the "cache validity period" in the node properties (5-10 minutes is recommended).
- Model Selection Optimization: Match appropriate models for different task phases, e.g. Claude-3-long for research tasks and GPT-4-turbo for code generation.
Advanced Tip: Use the platform's built-in Performance Analyzer (located in the debugging toolbar) to generate execution time heat maps to visualize bottleneck nodes.
This answer comes from the articleCommonGround: Building a Workflow Platform for Multi-Intelligence CollaborationThe
May not be reproduced without permission:AI productivity tools " How to improve the execution efficiency of multi-intelligence workflows?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil