rag-pipeline

Design an end-to-end RAG pipeline for document QA and knowledge retrieval.

Updated Apr 1, 2026

Search Tags:#rag #evaluation #pipeline #chunking #embedding #retrieval-augmented-generation

Ready to run

npx skills add https://github.com/hpsgd/turtlestack --skill rag-pipeline-hpsgd

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-pipeline
Source: https://github.com/hpsgd/turtlestack/tree/main/plugins/engineering/ai-engineer/skills/rag-pipeline
Command: npx skills add https://github.com/hpsgd/turtlestack --skill rag-pipeline-hpsgd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design a scalable, end-to-end RAG pipeline that orchestrates corpus analysis, chunking strategy, embedding selection, retrieval configuration, and evaluation to enable accurate, citation-backed QA over large knowledge bases.

Core Features & Use Cases

End-to-end RAG design including corpus profiling, chunking, metadata enrichment, embedding selection, and retrieval configuration.
Use cases include building QA assistants over manuals, technical documentation, and domain-specific corpora, with traceable citations and freshness handling.
Real-world example: deploy a RAG workflow to answer customer questions using a knowledge base of manuals and articles with grounding citations and performance monitoring.

Quick Start

Provide a small sample corpus, configure 512-token chunks with 10% overlap, select an embedding model, run 20 queries, and evaluate the results.

Frequently Asked Questions about rag-pipeline

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

FAQPage Schema

How do I design an end-to-end RAG pipeline for document question answering?▼

Designing a RAG pipeline involves orchestrating corpus analysis, chunking strategy, embedding selection, and retrieval configuration. This enables accurate, citation-backed question answering over large knowledge bases with traceable citations.

What is the best way to evaluate retrieval-augmented generation performance?▼

The best way to evaluate RAG performance uses a standardized prompt template aligned with RAGAS metrics. The workflow enforces an embedding evaluation workflow and retrieval performance targets to measure grounding and citation accuracy.

How do I configure chunking strategy and metadata enrichment for a knowledge base?▼

Configuring chunking strategy involves setting token limits like 512-token chunks with 10% overlap. Metadata enrichment and a formal metadata schema are then applied to structure diverse content types for effective retrieval.

Does this RAG pipeline handle freshness maintenance across diverse content types and languages?▼

Yes, the RAG pipeline handles freshness maintenance across diverse content types and languages. It continuously updates the knowledge base to ensure retrieval-augmented generation provides accurate and current answers.

Can I build a QA assistant over technical documentation with traceable citations?▼

Yes, you can build QA assistants over manuals, technical documentation, and domain-specific corpora. The pipeline generates answers using a knowledge base with grounding citations and performance monitoring.