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rag-pipeline-builder

Design end-to-end retrieval-augmented generation pipelines for document-based AI assistants.

5|Updated Dec 31, 2025
Search Tags:#rag#pipeline#generation#chunking#document#vector-store#retrieval-augmented
One-click install
npx skills add https://github.com/patricio0312rev/skillset --skill rag-pipeline-builder-patricio0312rev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-pipeline-builder
Source: https://github.com/patricio0312rev/skillset/tree/main/templates/ai-engineering/rag-pipeline-builder
Command: npx skills add https://github.com/patricio0312rev/skillset --skill rag-pipeline-builder-patricio0312rev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designs end-to-end retrieval-augmented generation pipelines for document-based AI assistants, enabling efficient retrieval and generation.

Core Features & Use Cases

  • Chunking strategies to segment documents into meaningful chunks with metadata.
  • Metadata schema, vector-store integration, retrieval, reranking, and evaluation plans.
  • Use cases include building knowledge bases, document search, and semantic search systems.

Quick Start

Instantiate a RAG pipeline by following the outlined setup to configure chunking, embeddings, and retrieval.

Frequently Asked Questions about rag-pipeline-builder

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

FAQPage Schema
How do I design a RAG pipeline for a document-based AI assistant?▼

Design a RAG pipeline by configuring chunking strategies, metadata schemas, vector store integration, hybrid retrieval, reranking, and evaluation plans to enable efficient document search and knowledge retrieval.

What chunking strategies should I use for semantic search systems?▼

Chunking strategies segment documents into meaningful chunks paired with metadata schemas, enabling precise semantic search and knowledge base retrieval for document AI assistants.

How does reranking improve retrieval-augmented generation pipelines?▼

Reranking refines retrieval-augmented generation pipelines by reordering retrieved document chunks based on relevance, improving the accuracy of knowledge retrieval and document search outputs.

Can I build a hybrid retrieval system for a knowledge base using this approach?▼

Yes, you can build a hybrid retrieval system for a knowledge base by setting up vector store integration and applying reranking and evaluation plans to document chunks.

What is the best way to plan evaluation for a retrieval-augmented generation pipeline?▼

Plan evaluation for a retrieval-augmented generation pipeline by defining metrics to assess chunking strategies, vector store retrieval accuracy, and reranking effectiveness within document search tasks.

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