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RAG architecture for academic knowledge retrieval and synthesis
Passed 0 of 23 test cases with the skill, and 23 of 23 without it.
0 fixed14 passed either way7 still failing2 broke
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Design and implement Retrieval-Augmented Generation (RAG) systems for academic research, including document chunking, embedding strategies, retrieval pipelines, and evaluation.
Retrieval-Augmented Generation (RAG) augments a language model's generation with relevant information retrieved from an external knowledge base. For academic research, this enables:
Query: "What are the main challenges of protein folding?"
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[1. Query Processing]
|-- Embed query using embedding model
|-- Optional: Query expansion / HyDE
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[2. Retrieval]
|-- Search vector database for top-k relevant chunks
|-- Optional: Reranking with cross-encoder
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[3. Context Assembly]
|-- Combine retrieved chunks into a prompt
|-- Add metadata (source, page, citation)
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[4. Generation]
|-- LLM generates answer grounded in retrieved context
|-- Include inline citations
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Answer with citations
test cases passed, vs 23 / 23 without
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