---
name: km-rag-methodology
description: Expertise in implementing Knowledge-Map RAG (KM-RAG), focusing on structured Knowledge Units, Graph relationships, and multi-stage retrieval in .NET.
tags: [RAG, KnowledgeMap, GraphRAG, AI, .NET, CleanArchitecture]
version: 1.0.0
---

# KM-RAG Methodology

This skill provides a comprehensive framework for transitioning from basic chunk-based RAG to a structured **Knowledge-Map RAG (KM-RAG)** approach.

## Core Concepts
- **Knowledge Units (KU)**: Granular pieces of information with stable IDs and types (Section, Table, Definition, Rule).
- **Knowledge Map (Graph)**: Explicit links between units (`Next`, `Defines`, `Contains`) enabling contextual expansion.
- **Multi-Stage Retrieval**: A pipeline starting with semantic candidate generation followed by graph expansion and optional reranking.
- **Provenance & Governance**: Full traceability of AI answers back to their source units.

## Key Artifacts
- [Core Concepts](artifacts/core_concepts.md): Deep dive into the methodology.
- [Implementation Patterns (.NET)](artifacts/implementation_patterns.md): C# code for units, links, and retrieval.
- [Quality Checklist](artifacts/evaluation_checklist.md): Metrics and safety procedures.
- [Deep Research Report](artifacts/deep-research-report-rag.md): Original research on the KM-RAG approach.

## Usage
Use this skill when:
- Designing or refactoring RAG systems for high precision.
- Implementing multi-tenant knowledge bases.
- Enhancing AI answers with structural context from a graph.
- Building evaluation pipelines for hallucination detection.