rag-pipeline

Implement end-to-end RAG pipelines with chunking, embeddings, and hybrid search.

3|Updated May 28, 2026

Search Tags:#llm #rag #provenance #embedding #retrieval-augmented-generation #vector-database

Ready to run

npx skills add https://github.com/mahg-es/araya --skill rag-pipeline-mahg-es

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-pipeline
Source: https://github.com/mahg-es/araya/tree/main/skills/rag-pipeline
Command: npx skills add https://github.com/mahg-es/araya --skill rag-pipeline-mahg-es

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Grounds LLM answers in retrieved documents to reduce hallucinations and increase citation reliability.

Core Features & Use Cases

End-to-end RAG pipeline design including chunking, embedding, vector storage, and retrieval.
Hybrid search & reranking using keyword and vector similarity to improve relevance with provenance.
Use case coverage for documentation, internal knowledge bases, and private data requiring citation-backed answers.

Quick Start

Configure a RAG pipeline with a sample document corpus, select an embedding model, and run a retrieval-backed answer with provenance.

Frequently Asked Questions about rag-pipeline

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

FAQPage Schema

How do I reduce LLM hallucinations using retrieved documents?▼

Retrieval-augmented generation grounds LLM answers in retrieved documents to reduce hallucinations and increase citation reliability for knowledge-intensive tasks. It applies provenance-aware prompts to ensure answers are backed by source data.

What is the best way to design an end-to-end RAG pipeline?▼

An end-to-end RAG pipeline implements chunking, embedding selection, vector databases, retrieval, reranking, and generation. Configuring these components ensures grounded answers with provenance for your document corpus or private data.

How does hybrid search and reranking improve retrieval-augmented generation?▼

Hybrid search and reranking improves retrieval-augmented generation by combining keyword and vector similarity to increase document relevance. This approach ensures provenance-aware prompts receive the most accurate context for citation-backed answers.

Can I use a RAG pipeline for internal knowledge bases and private data?▼

Yes, a RAG pipeline supports internal knowledge bases and private data requiring citation-backed answers. It applies chunking, embedding selection, and vector storage to ground LLM responses in your private documentation.

Do I need a vector database to implement retrieval-augmented generation?▼

Yes, a vector database is a core component of retrieval-augmented generation pipelines. It stores document embeddings and enables the vector similarity search required for retrieving relevant context before generation.

Why does my LLM output lack citation reliability for documentation queries?▼

Your LLM lacks citation reliability because it is not grounded in retrieved documents. Implementing a RAG pipeline with provenance-aware prompts and hybrid search grounds answers in source documentation to provide reliable citations.