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BeamusWayneBeamusWayneCommunity·2 Agent Skills Included

RAG-learning

Hands-on RAG tutorials, strategy demos, and evaluation projects

Teaches retrieval-augmented generation from basics to production through runnable Python examples. Covers LangChain fundamentals, HyDE, RAG Fusion, CRAG, Self-RAG, GraphRAG, and RAGAS evaluation with step-by-step demos. Eliminates scattered documentation hunting by organizing learning into four progressive stages with ready-to-run Streamlit apps. Helps learners compare five RAG strategies side by side and build real knowledge-base question-answering systems.
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npx skills add BeamusWayne/RAG-learning --all -g -y
Available:

Tells the AI agent how the repository is organized, which sub-project conventions to follow, and enforces a verified one-feature-at-a-time work loop with progress tracking across sessions.

All Skills in This Repository (2)

Pure Emerald Level Indicators
📦 In Repo
BeamusWayneBeamusWayne

add-telegram

Configure Telegram as a communication channel for NanoBob.

Community
Intermediate
📦 In Repo
BeamusWayneBeamusWayne

add-tui

Create a TUI channel for NanoBob with terminal-based chat.

Community
Intermediate

Frequently Asked Questions

FAQPage Schema
How to install RAG-learning?▼

Run `npx skills add BeamusWayne/RAG-learning --all -g -y` in your terminal to install all skills in this repository globally.

What can I learn from RAG-learning?▼

You learn retrieval-augmented generation from scratch, including document chunking, embeddings, vector search, and advanced strategies like HyDE, CRAG, Self-RAG, and GraphRAG through runnable demos.

How to compare different RAG strategies?▼

The MultiLevelRAG project provides a Streamlit app that routes questions across five RAG strategies and shows their answers side by side for direct comparison.

Does RAG-learning work with Claude Code and Cursor?▼

Yes. The included AGENTS.md and CLAUDE.md files give coding agents clear routing and verification rules, so they work reliably in Claude Code, Cursor, and Codex.

Is RAG-learning suitable for beginners?▼

Yes. It starts with a 9-module primer and 31 gradual LangChain examples, then progresses to production-level projects, so no prior RAG experience is required.

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