quality-enriched-prompting

To optimize prompt quality across multiple dimensions, you can use multi-dimensional metrics and LLM-based evaluation to guide continuous improvements. This approach applies Pareto frontier analysis to identify optimal prompts.

0,1-enriched category theory models prompt transformations as quality-rated morphisms. It maps prompts to continuous quality scores using enriched functors, enabling structured evaluation and comparison of LLM outputs.

Define an initial prompt and an evaluation function, then run iterative improvement until the desired quality is reached. The framework uses LLM evaluation to co-create higher-quality prompts through enrichment-based composition.

Yes, Pareto frontier analysis supports domain-agnostic quality control by identifying optimal prompts across multiple dimensions. It evaluates trade-offs between different quality metrics to find the best performing prompts.

No specific dependencies are required to implement enriched category prompt optimization. The framework operates independently to model prompt transformations and map them to continuous quality scores using enriched functors.

Use multi-dimensional metrics when prompt quality involves trade-offs across different criteria. This approach models transformations as quality-graded morphisms, providing a richer evaluation than single scores by mapping to a Pareto frontier.