embedding-optimization

SKILL.md

Optimize embedding generation for cost, performance, and quality in RAG and semantic search systems.

For detailed decision frameworks including cost comparisons, quality benchmarks, and data privacy considerations, see references/model-selection-guide.md.

| Model | Type | Dimensions | Cost per 1M tokens | Best For |

Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings. Source: ancoleman/ai-design-components.

View raw