Embedding Strategies
Choose and optimize embedding models for semantic search and RAG. Compares model trade-offs, designs chunking strategies, handles domain adaptation, and tunes embedding quality to improve retrieval accuracy.
This skill helps you pick the right embedding model and get the most from it. It compares model dimensions, context length, and cost, designs chunking and overlap strategies for your content, evaluates retrieval quality with recall metrics, and covers domain adaptation, hybrid search, and reranking to sharpen relevance.
embeddings semantic-search chunking rag retrieval
When to use
Use when selecting an embedding model, designing chunking, or improving retrieval quality in a semantic search or RAG system that returns irrelevant results.
Examples
Pick an embedding model
Match model to use case
Help me choose between OpenAI, Cohere, and open-source embedding models for multilingual product search on a budget
Fix poor retrieval
Improve chunking and recall
My RAG retrieval misses relevant passages — recommend a chunking strategy, overlap, and reranking to raise recall