ai-rag-patterns

What problem does it solve?

This Skill helps you design, tune, and troubleshoot production Retrieval-Augmented Generation (RAG) pipelines so retrieval failures get fixed at the correct layer with measurable results.

Core Features & Use Cases

• Chunking strategy selection: Choose fixed/recursive, semantic, late chunking, Anthropic Contextual Retrieval, and parent-document approaches to improve what can be retrieved.
• Retrieval and fusion tuning: Combine BM25 and dense retrieval with reciprocal rank fusion and related hybrid methods to improve candidate recall.
• Reranking and evaluation: Apply cross-encoders or rerankers on top candidates and evaluate with RAGAS, TruLens RAG Triad, or DeepEval to quantify impact and diagnose the seven common RAG failure modes.
• Agentic and multi-modal RAG patterns: Use agentic retrieval loops and multi-modal retrieval approaches like ColPali for document-image-heavy corpora.

Quick Start

Use the ai-rag-patterns skill to diagnose why my RAG agent is missing relevant information and propose a production-grade chunking, hybrid retrieval, reranking, and evaluation plan with cited metrics.