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rag-implementer

Implements RAG pipelines with hybrid search and re-ranking.

14|5|Updated Jan 29, 2026
Search Tags:#llm#rag#vector database#knowledge base#retrieval#embedding
One-click install
npx skills add https://github.com/oakoss/agent-skills --skill rag-implementer-oakoss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-implementer
Source: https://github.com/oakoss/agent-skills/tree/main/skills/rag-implementer
Command: npx skills add https://github.com/oakoss/agent-skills --skill rag-implementer-oakoss

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you build production-ready retrieval-augmented generation (RAG) systems, enabling LLMs to access and utilize external knowledge.

Core Features & Use Cases

  • RAG Pipeline Implementation: Guides through embedding, vector storage, retrieval, and generation stages.
  • Advanced Techniques: Supports hybrid search, re-ranking, contextual chunking, and GraphRAG.
  • Use Case: You need to build a Q&A system for your company's internal documentation. This Skill will help you choose the right embedding model, set up a vector database, and implement a robust retrieval strategy to ensure accurate and grounded answers.

Quick Start

Use the rag-implementer skill to implement a retrieval pipeline with hybrid search and re-ranking.

Frequently Asked Questions about rag-implementer

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

FAQPage Schema
What is a RAG pipeline and how does it help an LLM access external knowledge?▼

A RAG pipeline helps an LLM access external knowledge by chunking documents, generating embeddings, storing them in a vector database, and retrieving relevant context to ground the model's generated answers.

How do I build a retrieval-augmented generation system for internal documentation?▼

To build a retrieval-augmented generation system, you implement chunking strategies, select an embedding model, configure a vector store, and apply retrieval techniques like hybrid search and re-ranking to ground answers in your internal documentation.

What's the best way to improve retrieval accuracy in a RAG knowledge base?▼

The best way to improve retrieval accuracy in a RAG knowledge base is by applying advanced techniques like hybrid search, re-ranking, and contextual chunking to refine the retrieved context before passing it to the LLM.

Does this RAG implementation support GraphRAG and hybrid search?▼

Yes, this RAG implementation supports advanced techniques including GraphRAG, hybrid search, and re-ranking to enhance document retrieval performance and provide more accurate, context-aware results for your LLM.

When should I use contextual chunking instead of standard document chunking?▼

You should use contextual chunking instead of standard document chunking when your RAG pipeline requires higher retrieval precision, as it helps preserve the semantic context of document segments during the embedding process.

How do I evaluate and govern a production-ready RAG pipeline?▼

You evaluate and govern a production-ready RAG pipeline by addressing knowledge base architecture selection, applying retrieval evaluation metrics, and implementing governance protocols to ensure accurate and grounded LLM outputs.

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