rag-implementation — Skills

What it does

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

Build knowledge-grounded LLM applications with vector databases, semantic search, and retrieval strategies.

Source

Repository: https://github.com/wshobson/agents

rag-implementation FAQ

What does the rag-implementation skill do?

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. Building Q&A systems over proprietary documents Creating chatbots with current, factual information

How do I install rag-implementation?

Run: npx -y skills add https://github.com/wshobson/agents --skill rag-implementation --agent claude-code — the source lives at github.com/wshobson/agents.

Maintain rag-implementation?

Let people know it's listed here — add the badge (live metrics, light/dark aware) or a plain link to your README or docs.

[![rag-implementation on getagentictools](https://getagentictools.com/badge/skills/wshobson-agents-rag-implementation.svg)](https://getagentictools.com/skills/wshobson-agents-rag-implementation?ref=badge)

npx agentictools info skills/wshobson-agents-rag-implementation

The second line is the CLI lookup for this page — handy in READMEs and docs.