building-rag-pipelines

Design RAG pipelines with hybrid search, reranking, and relevance feedback.

1|2|Updated Feb 5, 2026

Search Tags:#ai #llm #rag #search #knowledge base #retrieval

Ready to run

npx skills add https://github.com/7a336e6e/skills --skill building-rag-pipelines

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: building-rag-pipelines
Source: https://github.com/7a336e6e/skills/tree/main/ai-rag/building-rag-pipelines
Command: npx skills add https://github.com/7a336e6e/skills --skill building-rag-pipelines

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of building robust and intelligent Retrieval-Augmented Generation (RAG) systems that can accurately answer questions from large document sets, adapt to new information, and operate with advanced reasoning capabilities.

Core Features & Use Cases

Hybrid Search: Combines vector and keyword search for comprehensive retrieval.
Reranking: Improves precision by re-evaluating search results.
Agentic Patterns: Enables iterative reasoning and tool use for complex queries.
Continuous Learning: Incorporates relevance feedback to improve over time.
Use Case: Develop an AI assistant that can answer complex technical questions by querying internal documentation, code repositories, and system logs, providing precise and context-aware answers.

Quick Start

Design and implement a RAG pipeline that combines hybrid search and reranking for accurate document retrieval.

Frequently Asked Questions about building-rag-pipelines

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

FAQPage Schema

How do I build a RAG pipeline with hybrid search and reranking?▼

To build a RAG pipeline with hybrid search and reranking, combine dense and sparse retrieval for comprehensive document fetching, then apply cross-encoder reranking to re-evaluate results and improve precision before generation.

What is continuous learning in retrieval-augmented generation systems?▼

Continuous learning in retrieval-augmented generation systems incorporates relevance feedback from user interactions to iteratively improve retrieval precision and recall over time without full model retraining.

When do I need agentic reasoning patterns in a RAG system?▼

Agentic reasoning patterns are needed in a RAG system when handling complex queries that require iterative reasoning and tool use to synthesize context-aware answers from technical documentation or system logs.

How does entity extraction improve retrieval precision in a knowledge base?▼

Entity extraction improves retrieval precision in a knowledge base by identifying and structuring key entities from queries and documents, enabling iterative query enhancement for more targeted search results.

Can I use cross-encoder reranking with keyword and vector search?▼

Yes, cross-encoder reranking integrates directly with hybrid search setups, re-evaluating the combined dense and sparse retrieval results to boost accuracy and context relevance.

What is the best way to answer complex technical questions from internal documentation?▼

The best way to answer complex technical questions from internal documentation is deploying a production-quality RAG pipeline featuring hybrid search, agentic patterns, and continuous learning mechanisms for precise context extraction.