agentsop-llamaindex

What problem does it solve?

This skill helps agents design and debug LlamaIndex-based RAG systems reliably, turning messy document corpora into grounded retrieval answers while preventing common ingestion, retrieval, and evaluation failure modes.

Core Features & Use Cases

• LlamaIndex mental model distillation: Converts LlamaIndex’s Documents → Nodes → Index → Retriever → Query Engine / Response Synthesizer architecture into an actionable decision framework.
• RAG bootstrap SOP + hardening playbook: Provides a step-by-step protocol for baseline indexing, eval-loop setup, and iterative optimization (chunking, embeddings, hybrid retrieval, reranking, routing, and production hardening).
• Dilemma-driven decision support: Covers key architecture tradeoffs such as chunk precision vs context, hybrid BM25+dense vs dense-only, router vs agent vs decomposition, long-context vs RAG, and sentence-window vs auto-merging.

Quick Start

Tell an AI agent: "Please activate the agentsop-llamaindex skill to design a production-ready RAG pipeline in LlamaIndex for my private documents, include an eval loop, and explain which index/retrieval primitives to use for my query types."