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rag-implementation

combined

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases. RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

When to use

• Building Q&A systems over proprietary documents
• Creating chatbots with current, factual information
• Implementing semantic search with natural language queries
• Reducing hallucinations with grounded responses
• Enabling LLMs to access domain-specific knowledge
• Building documentation assistants
• Creating research tools with source citation
• Building RAG-powered applications

When not to use

• You only need purely generative writing without retrieval
• The dataset is too small to justify embeddings
• You cannot store or process the source data safely
• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The three copies overlap on the core RAG pipeline (embeddings, vector store, chunking, retrieval, reranking, evaluation), but each contains material steps or constraints the others lack. rmyndharis/antigravity-skills provides a concise 4-step procedure plus safety constraints (redaction, access controls, source-document exposure) and a LangChain quick start. sickn33/agentic-awesome-skills adds a phased workflow with explicit phases for LLM integration, caching, and evaluation, plus quality gates and limitations. LeoYeAI/openclaw-master-skills provides a LangGraph quick start, a 2026 embedding model table, and a different vector database option (pgvector). Crowning one copy would hide the other two's unique steps and constraints.