A closed-loop methodology for cue word quality improvement
AI-Gist provides full process optimization tools from generation to iteration:
generation phase
When using "AI Assisted Generation":
1. Five elements of input: target audience + output format + content length + style requirements + key constraints
2. Enable "multi-program generation" mode (default generation of 3 candidate versions)
3. Use of the "Prompt Scoring" function (scoring based on the three dimensions of clarity/specificity/completeness)
Optimization phase
1. Diagnostic tool: run "Effectiveness Analysis" to generate a quality report (including indicators such as token distribution/variable coverage)
2. Variable Optimization: Set the variables with high frequency modification as "Smart Variables", and the system will recommend candidate values according to the usage records.
3. A/B testing: Push different versions to the "test sandbox" and automatically collect feedback on the quality of the AI output of each version.
continual improvement
It is recommended that a knowledge base of cue words be established:
1. Add "best practices" tags to quality prompts
2. Record the applicable model for each cue word (GPT-4/Claude, etc.)
3. Run regular "template health checks"
This answer comes from the articleAI-Gist: a privacy-first AI cue word management toolThe
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