agentic-rag

• SKILL.mdcore · 4,472 bytes

# Agentic RAG - Self-Correcting Retrieval

## Overview
Agentic RAG evolves beyond static information retrieval by embedding autonomous agents within the retrieval and generation pipeline. Unlike "Naive RAG" which assumes a direct mapping from query to document chunk, Agentic RAG employs iterative reasoning, self-correction, and multi-step workflows to ensure answers are grounded, accurate, and comprehensive. It treats retrieval as a dynamic task-oriented process.

## When To Use
- When dealing with multi-hop questions requiring information synthesis from disparate sources.
- When existing RAG pipelines suffer from high hallucination rates or low retrieval precision.
- When the domain requires "Codebase RAG" that understands syntax, imports, and symbol definitions rather than just text semantic similarity.
- When you need systems that can autonomously fall back to web search or tool execution when internal knowledge is insufficient.

## Architecture Patterns
1. **Query Decomposition & Routing**: Breaking down complex, high-level questions into focused sub-queries. Agents route these sub-queries to appropriate specialized indexes (e.g., code-index, docs-index, general-web).
2. **Hybrid Retrieval + RRF**: Combining lexical search (BM25 for acronyms/technical IDs) with dense embedding search (vector similarity), merged using Reciprocal Rank Fusion (RRF) to boost ranking robustness.
3. **Corrective RAG (CRAG)**: Implementing a relevance grader that evaluates retrieved docs. If quality is low, the agent triggers a fallback workflow (e.g., web search, re-phrasing).