LlamaIndex · SOP

Third-person analytical view of how LlamaIndex thinks about turning private documents into a grounded answering system. The skill is for an LLM agent that writes / reviews / debugs RAG code — not for an end user reading docs.

何时激活 (Activation Rules)

Activate this skill when any of the following holds:

1. The user's request involves building, modifying, or debugging a RAG pipeline (retrieval over private/unstructured data + LLM synthesis).
2. The user mentions LlamaIndex (from llama_index...), LlamaParse, LlamaCloud, or a LlamaIndex-style primitive (VectorStoreIndex, SummaryIndex, IngestionPipeline, QueryEngine, SubQuestionQueryEngine, RouterQueryEngine, Settings, Workflows).
3. The user is comparing RAG frameworks (LlamaIndex vs LangChain vs Haystack vs raw vector store).
4. The user is choosing between stuffing context, RAG, or an agent for a knowledge task.
5. The user is debugging retrieval quality (hallucinations, wrong chunks, stale data, embedding drift) — even if the codebase predates LlamaIndex, the failure-mode taxonomy applies.
6. The user is evaluating a RAG system (faithfulness, relevancy, MRR, hit-rate).