verify-rag-pipeline

Validates RAG pipeline response composition, citations, and document download safety.

Updated Feb 12, 2026

Search Tags:#testing #quality assurance #verification #llm #rag #orchestration #pipeline

Ready to run

npx skills add https://github.com/bigbulgogiburger/rag-bio --skill verify-rag-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: verify-rag-pipeline
Source: https://github.com/bigbulgogiburger/rag-bio/tree/main/.claude/skills/verify-rag-pipeline
Command: npx skills add https://github.com/bigbulgogiburger/rag-bio --skill verify-rag-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures the reliability and correctness of the RAG pipeline by systematically verifying critical components and logic, preventing issues like inaccurate answers, broken citations, or unsafe document downloads.

Core Features & Use Cases

Comprehensive Verification: Checks system prompts, response formatting, citation consistency, data integrity, API safety, and more.
Targeted Testing: Designed to be run after specific code modifications in the RAG pipeline, ensuring changes haven't introduced regressions.
Use Case: After updating the prompt template for AI-generated answers, run this Skill to confirm that the new prompt adheres to all formatting rules, citation standards, and doesn't introduce unintended biases or hedging.

Quick Start

Run the verify-rag-pipeline skill to check the integrity of the RAG pipeline's response composition and document download functionalities.

Frequently Asked Questions about verify-rag-pipeline

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

FAQPage Schema

How do I verify the accuracy and integrity of a RAG pipeline's responses?▼

Verifying RAG pipeline integrity involves systematically checking response composition, citation consistency, and evidence completeness. This Skill validates system prompts, formatting rules, and API safety to prevent inaccurate answers and broken citations after code modifications.

What does RAG pipeline verification check for in LLM prompt neutrality and guardrails?▼

RAG pipeline verification checks LLM prompt neutrality, guardrail application, verdict thresholds, and question decomposition patterns. It ensures prompts adhere to formatting standards without introducing unintended biases, hedging, or unsafe API behaviors during response generation.

How do I test RAG retrieval fallback mechanisms and edge cases like I don't know paths?▼

Testing RAG retrieval fallback mechanisms involves validating indexing guards, sub-question completeness, and "I Don't Know" paths. This Skill systematically checks these edge cases alongside vector search filter conversion and multi-product filter searches to ensure robust query handling.

Can I use this to check citation consistency and evidence mapping in a multi-product RAG system?▼

Yes, you can check citation consistency and per-question evidence mapping in a multi-product RAG system. This Skill verifies multi-product filter searches, product name extraction, and Korean query normalization to ensure accurate evidence summarization across diverse query patterns.

Why does my RAG pipeline return incomplete evidence after updating the prompt template?▼

Incomplete evidence after updating prompt templates often stems from broken citation consistency, N+1 query issues, or flawed question decomposition. Run RAG pipeline verification to confirm new prompts adhere to formatting rules and evidence mapping standards without introducing regressions.