Agent skill · research science · sickn33
rag-implementation
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
Why this skill is useful
Adds a comprehensive workflow for implementing RAG systems, including specific strategies for embedding selection, vector database setup, and retrieval optimization that are not commonly documented.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
RAG Implementation Workflow Overview Specialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation. When to Use This Workflow Use this workflow when: Building RAG-powered applications Implementing semantic search Creating knowledge-grounded AI Setting up document Q&A systems Optimizing retrieval quality Workflow Phases Phase 1: Requirements Analysis Skills to Invoke ai-product - AI product design rag-engineer - RAG engineering Actions 1. Define use case 2. Identify data sources 3. Set accuracy requirements 4. Determine latency targets 5. Plan evaluation metrics Copy-Paste Prompts Phase 2: Embedding Selection Skills to Invoke embedding-strategies - Embedding selection rag-engineer - RAG patterns Actions 1. Evaluate embedding models 2. Test domain relevance 3. Measure embedding quality 4. Consider cost/latency 5. Select model Copy-Paste Prompts Phase 3: Vector Database Setup Skills to Invoke vector-database-engineer - Vector DB similarity-search-patterns - Similarity search Actions 1. Choose vector database 2. Design schema 3. Configure indexes 4. Set up connection 5. Test queries Copy-Paste Prompts Phase 4: Chunking Strategy Skills to Invoke rag-engineer - Chunking strategies rag-implementation - RAG implementation Actions 1. Choose chunk size 2. Implement chunking 3. Add overlap handling 4. Create metadata 5. Test retrieval quality Copy-Paste Prompts Phase 5: Retrieval Implementation Skills to Invoke similarity-search-patterns - Similarity search hybrid-search-implementation - Hybrid search Actions 1. Implement vector search 2. Add keyword search 3. Configure hybrid search 4. Set up reranking 5. Optimize latency Copy-Paste Prompts Phase 6: LLM Integration Skills to Invoke llm-application-dev-ai-assistant - LLM integration llm-application-dev-prompt-optimize - Prompt optimization Actions 1. Select LLM provider 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/rag-implementation