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corrective-rag

Implement Corrective RAG with retrieval validation and web search fallbacks.

5|1|Updated Feb 4, 2026
Search Tags:#llm#rag#retrieval#grounding#langgraph#self-correction
One-click install
npx skills add https://github.com/latestaiagents/agent-skills --skill corrective-rag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: corrective-rag
Source: https://github.com/latestaiagents/agent-skills/tree/main/plugins/rag-architect/skills/corrective-rag
Command: npx skills add https://github.com/latestaiagents/agent-skills --skill corrective-rag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the unreliability of standard RAG systems by implementing a robust framework for validating retrieved information and automatically correcting errors, ensuring higher quality and more grounded AI responses.

Core Features & Use Cases

  • Retrieval Validation: Assesses the relevance and accuracy of retrieved documents against the user's query.
  • Fallback Strategies: Integrates web search as a fallback when initial retrieval is insufficient or incorrect.
  • Self-Correction: Refines knowledge by extracting only pertinent information and generates answers based on validated and augmented data.
  • Use Case: When building a customer support chatbot that must provide accurate product information, this skill ensures that the AI only uses verified data, falling back to a web search if internal knowledge is ambiguous or missing, and then synthesizing a precise answer.

Quick Start

Use the corrective-rag skill to answer the question "What is the latest LangChain version?"

Frequently Asked Questions about corrective-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does self-correction improve RAG accuracy?▼

Self-correction improves RAG accuracy by grading retrieved document relevance, triggering web search fallbacks for ambiguous data, and refining knowledge to synthesize validated, grounded responses.

How do I implement a fallback web search when RAG retrieval fails?▼

You can implement a web search fallback by orchestrating a LangGraph pipeline that grades retrieval relevance and automatically routes to web search when internal knowledge is insufficient or incorrect.

What is Corrective Retrieval Augmented Generation for LLM pipelines?▼

Corrective Retrieval Augmented Generation (CRAG) is a framework that enhances LLM pipeline reliability by validating retrieved information, triggering fallback mechanisms, and refining knowledge to ensure high-accuracy grounded responses.

Can I use LangGraph to orchestrate retrieval validation and knowledge refinement?▼

Yes, you can use LangGraph to orchestrate the full CRAG pipeline, coordinating retrieval validation, web search fallbacks, and knowledge refinement to automatically correct errors in RAG systems.

Why does my RAG system return ungrounded or incorrect answers?▼

RAG systems return ungrounded answers when retrieval validation is missing, but implementing relevance grading and self-correction mechanisms ensures the LLM only uses verified data to synthesize responses.

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