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rag-architect

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Build scalable RAG systems and vector stores.

Data & Analytics#semantic search#embeddings#knowledge base#RAG#retrieval#vector-database
AuthorJeffallan
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill designs Retrieval-Augmented Generation (RAG) architectures, vector stores, and knowledge-grounded AI pipelines to ground LLM outputs and accelerate accurate retrieval.

Core Features & Use Cases

  • Vector store design & selection: Choose appropriate vector DB and schema.
  • Chunking & metadata: Plan document chunking with boundaries and enrichment.
  • Retrieval pipeline: Embeddings, hybrid search, reranking.
  • Evaluation & monitoring: Measure precision, latency, etc.

Quick Start

Draft a RAG system blueprint for a knowledge-base chatbot with a vector store, ingest workflow, and retrieval path.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: rag-architect
Download link: https://github.com/Jeffallan/claude-skills/archive/main.zip#rag-architect

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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