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rag-architect

Design and implement end-to-end RAG pipelines for context-aware LLM responses.

Updated Apr 20, 2026
Search Tags:#embeddings#rag#semantic-search#vector-databases#retrieval-augmented-generation#document-qa
One-click install
npx skills add https://github.com/Godhand-Arnav/Scalar-finals --skill rag-architect-godhand-arnav
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-architect
Source: https://github.com/Godhand-Arnav/Scalar-finals/tree/main/.claude/skills/rag-architect
Command: npx skills add https://github.com/Godhand-Arnav/Scalar-finals --skill rag-architect-godhand-arnav

SYSTEM DOCUMENTATION & REQUIREMENTS

## What problem does it solve? Design and implement Retrieval-Augmented Generation (RAG) systems to handle document Q&A, knowledge bases, and semantic search by enabling context-aware LLM responses.

## Core Features & Use Cases

  • Document chunking strategies, embedding model selection, vector database choices, and evaluation frameworks.
  • Retrieval methods (dense, sparse, hybrid), query enhancement techniques, and end-to-end RAG pipeline patterns.
  • Use cases include enterprise knowledge bases, literature reviews, and large-scale retrieval with citations.

Quick Start

Provide a starter blueprint for a RAG pipeline and outline the first steps to implement document retrieval in an LLM workflow.

Frequently Asked Questions about rag-architect

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

FAQPage Schema
How do I build a retrieval-augmented generation pipeline for document QA?▼

You build a retrieval-augmented generation pipeline by applying document chunking, generating embeddings, storing them in a vector database, and retrieving relevant context to augment LLM responses for document QA.

What is the best way to choose a vector database for semantic search?▼

Choosing a vector database for semantic search depends on evaluating vector store options against your specific retrieval methods, scale requirements, and the embedding models used in your retrieval-augmented generation system.

How does document chunking affect retrieval-augmented generation systems?▼

Document chunking affects retrieval-augmented generation by determining how text is segmented before embedding, which directly impacts the accuracy of semantic search and the relevance of context retrieved for LLM responses.

Can I use hybrid retrieval methods to improve context-aware LLM responses?▼

Yes, you can use hybrid retrieval methods, combining dense and sparse techniques, to improve context-aware LLM responses by enhancing the accuracy and coverage of retrieved documents in your pipeline.

What evaluation frameworks are needed for retrieval-augmented generation pipelines?▼

Evaluation frameworks for retrieval-augmented generation pipelines are needed to measure the effectiveness of retrieval methods, embedding models, and chunking strategies to ensure accurate context-aware LLM responses.

When should I use query enhancement techniques in semantic search?▼

You should use query enhancement techniques in semantic search when standard retrieval methods fail to capture user intent, helping to refine queries and improve the relevance of context retrieved for LLM augmentation.

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