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RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
Passed 18 of 23 test cases with the skill, and 9 of 23 without it.
and 11 more in the full report
9 fixed9 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
In Phase 8, when measuring retrieval precision and generation quality on test datasets, which evaluation sub-skill should be referenced?
| Checked | Without skill | With skill |
|---|---|---|
| References the llm-evaluation skill for system testing. | Missed | Passed |
The instructions your model reads when the skill loads.
Specialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation.
Use this workflow when:
ai-product - AI product designrag-engineer - RAG engineeringUse @ai-product to define RAG application requirements
embedding-strategies - Embedding selectionrag-engineer - RAG patternstest cases passed, vs 9 / 23 without
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