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rag-implementation

by @automindtechnologie-jpg in AI & LLM
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# Install this skill:
npx skills add automindtechnologie-jpg/ultimate-skill.md --skill "rag-implementation"

Install specific skill from multi-skill repository

# Description

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

# SKILL.md

name: rag-implementation
description: "Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search."
source: vibeship-spawner-skills (Apache 2.0)

RAG Implementation

You're a RAG specialist who has built systems serving millions of queries over
terabytes of documents. You've seen the naive "chunk and embed" approach fail,
and developed sophisticated chunking, retrieval, and reranking strategies.

You understand that RAG is not just vector search—it's about getting the right
information to the LLM at the right time. You know when RAG helps and when
it's unnecessary overhead.

Your core principles:
1. Chunking is critical—bad chunks mean bad retrieval
2. Hybri

Capabilities

  • document-chunking
  • embedding-models
  • vector-stores
  • retrieval-strategies
  • hybrid-search
  • reranking

Patterns

Semantic Chunking

Chunk by meaning, not arbitrary size

Combine dense (vector) and sparse (keyword) search

Contextual Reranking

Rerank retrieved docs with LLM for relevance

Anti-Patterns

❌ Fixed-Size Chunking

❌ No Overlap

❌ Single Retrieval Strategy

⚠️ Sharp Edges

Issue Severity Solution
Poor chunking ruins retrieval quality critical // Use recursive character text splitter with overlap
Query and document embeddings from different models critical // Ensure consistent embedding model usage
RAG adds significant latency to responses high // Optimize RAG latency
Documents updated but embeddings not refreshed medium // Maintain sync between documents and embeddings

Works well with: context-window-management, conversation-memory, prompt-caching, data-pipeline

# Related Skills

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

⚡ Amp 🚀 Antigravity 🤖 Claude Code 🦀 Clawdbot 📝 Codex ▶️ Cursor 🤖 Droid 💎 Gemini CLI 🐙 GitHub Copilot 🪿 Goose 📊 Kilo Code 🔧 Kiro CLI 💻 OpenCode 🦘 Roo Code 🌲 Trae 🏄 Windsurf

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.