RAG Learning Academy

A structured, multi-agent learning environment for mastering Retrieval-Augmented Generation (RAG) — powered by Claude Code.

Inspired by Claude Code Game Studios, reimagined as an interactive learning guide for RAG systems.

What Is This?

RAG Learning Academy transforms Claude Code into a personal RAG tutor with 20 specialized AI agents, 22 interactive slash commands, and a 9-module curriculum that takes you from "what is RAG?" to production deployment.

Every concept is paired with a hands-on exercise. Every exercise is paired with evaluation metrics. You learn by building.

Quick Start

git clone https://github.com/TakaGoto/rag-learning-academy.git
cd rag-learning-academy
claude                         # Open Claude Code
/start                         # Begin your learning journey

That's it. No API keys, no pip install, no setup. Dependencies get installed when you need them (the first hands-on lesson will guide you).

What's Inside

20 Specialist Agents

Agents don't auto-route — they answer directly when asked, and Claude will suggest the right specialist when a question goes deep into their domain.

Tier	Count	Role	Model
Directors	3	Curriculum, Architecture, Research	opus
Domain Leads	5	Embedding, Retrieval, Indexing, Evaluation, Integration	opus
Specialists	12	Chunking, Vector DB, Reranking, Prompt Engineering, Hybrid Search, Document Parsing, Metadata, Query Analysis, Deployment, Evaluation Metrics, Graph RAG, Multimodal	sonnet

22 Slash Commands

Command	What It Does
/start	Assess your level, pick a track, get a working pipeline
/lesson	Start or continue a lesson (checkpoint quizzes between modules)
/quiz	Test your understanding
/build	Build a RAG component step by step
/evaluate	Run metrics on your pipeline
/debug-rag	Diagnose common RAG failures
/compare	Compare two approaches with live side-by-side output diffs
/benchmark	Benchmark pipeline performance
/architecture	Design a RAG system for a use case
/paper-review	Walk through a research paper
/code-review	Get feedback on your RAG code
/glossary	Look up RAG terminology
/challenge	Take on a hands-on challenge
/explain	Deep-dive into any concept (supports ELI5 mode)
/roadmap	View progress, badges, streaks, and export GitHub badges
/triage	Not sure where to go? Get routed to the right skill
/audit-content	Check materials for outdated references
/recap	Quick summary of what you covered last session
/sandbox	Spin up a minimal RAG pipeline instantly
/break-it	Learn by debugging intentionally broken pipelines
/fix	Skip the teaching, diagnose and fix your pipeline
/journal	Write notes about what clicked or confused you

9-Module Curriculum

Each lesson is tagged core or optional. The core path (~8.5 hours) gets you to a working, evaluated RAG system. Optional lessons add depth when you're ready.

#	Module	Lessons	Core	Key Topics
1	Foundations	4	4	What is RAG, architecture, RAG vs fine-tuning
2	Document Processing	5	2	Parsing, chunking strategies, metadata
3	Embeddings	5	2	Models, vector spaces, similarity, fine-tuning
4	Vector Databases	5	2	Chroma, Pinecone, pgvector, indexing
5	Retrieval Strategies	5	1	Dense, sparse, hybrid, reranking, MMR
6	Generation	5	2	Prompt engineering, grounding, citations
7	Evaluation	5	2	RAGAS, retrieval/generation metrics
8	Advanced Patterns	5	0	Agentic RAG, Graph RAG, CRAG, multimodal
9	Production	5	0	Deployment, caching, monitoring, scaling

Core path: ~8.5 hours | Full curriculum: ~31 hours

8 Milestones + 4 Proficiency Levels

The curriculum is broken into milestones — concrete checkpoints that mark real capability, not just lessons read.

#	Milestone	You Can Now...
1	First Light	Build a working RAG system from scratch
2	Data Wrangler	Turn any document into retrieval-ready chunks
3	Vector Navigator	Store and search embeddings effectively
4	Retrieval Engineer	Find the right information for any query
5	Prompt Architect	Generate grounded, cited answers
6	Quality Guardian	Measure everything and improve with data
7	Pattern Master	Go beyond basic RAG when it's warranted
8	Production Ready	Deploy, monitor, and scale a RAG system

Complete milestones to earn proficiency levels:

Level	Milestones	What It Means
RAG Explorer	1-2	Can build a basic pipeline and process documents
RAG Practitioner	3-5	Can design retrieval systems with proper search and prompting
RAG Engineer	6-7	Can evaluate, optimize, and apply advanced patterns
RAG Architect	8 + bonus	Can deploy, scale, and maintain production systems

See milestones.md for full requirements and bonus milestones.

3 Guided Projects

Level	Project	What You Build
Starter	Simple Q&A	Basic RAG pipeline over documents
Intermediate	Multi-Source Hybrid	Hybrid search + reranking + citations
Advanced	Agentic RAG	Self-correcting RAG with routing and tools

Progress Dashboard

Run make dashboard to generate an HTML progress page showing your milestones, proficiency level, module completion, and quiz scores.

Tech Stack

No API keys required to start. The defaults work out of the box:

Component	Default (zero config)	Optional Upgrade
LLM	Claude Code (you're already running it)	Ollama (local, needs 8-16GB RAM)
Embeddings	all-MiniLM-L6-v2 (local, no key)	OpenAI text-embedding-3-small
Vector DB	ChromaDB (local, no setup)	Pinecone, pgvector, Qdrant
Framework	LangChain	LlamaIndex
Evaluation	RAGAS	Custom metrics
Docs	PyPDF, BeautifulSoup	Unstructured, pdfplumber

Running models locally? Ollama is free but needs 8-16GB RAM for LLMs. Close heavy apps before running. If your machine slows down, switch to the API path. Local embedding models (all-MiniLM-L6-v2) are lightweight and run fine on most machines.

Project Structure

rag-learning-academy/
├── CLAUDE.md                    # Master config, voice & tone, agent behavior
├── .claude/
│   ├── settings.json            # Hooks, permissions
│   ├── agents/                  # 20 specialist agents
│   ├── skills/                  # 22 slash commands
│   ├── hooks/                   # Freshness checks, validation scripts
│   ├── rules/                   # Path-scoped coding standards
│   └── docs/
│       ├── curriculum/          # 9-module learning path (core/optional tagged)
│       ├── reference/           # Glossary, roster, milestones, standards
│       └── templates/           # Architecture, eval, project templates
├── src/                         # Your RAG code goes here
├── scripts/                     # Dashboard generator, utilities
├── projects/                    # Guided build projects
├── tests/                       # 616 structural + content tests
├── data/                        # Sample documents (6 files incl. 1,247-line chunking doc)
└── progress/                    # Your learning progress + dashboard

Learning Tracks

Run /start and you'll be assessed into one of three tracks:

Track	Score	Modules	Level Earned	Est. Hours
Beginner	0-3	1 → 4	RAG Explorer	15-25
Intermediate	4-6	3 → 7	RAG Practitioner	25-40
Advanced	7-10	6 → 9	RAG Engineer	30-45

Not sure where to go? Run /triage to get routed based on your situation.

Content Freshness

Academy materials are actively monitored so nothing goes stale:

• Weekly CI — checks PyPI versions, deprecated patterns, MTEB model health, review cycles (details)
• Monthly CI — content age report, creates GitHub issues for stale files
• On-demand — run /audit-content for a deep review with web search verification
• Frontmatter — every content file has last_reviewed, review_cycle, and staleness_risk metadata

Philosophy

"Understand → Build → Evaluate → Iterate"

• Agents teach, they don't just code for you
• Every concept comes with a hands-on exercise
• Every exercise has evaluation criteria
• You choose your path — agents advise, you decide
• The tone is conversational and direct — like learning from a smart friend, not reading a textbook

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines on:

• Reporting bugs and requesting features
• Development setup and running tests (make ci)
• Pull request process and checklist
• Content guidelines and voice & tone

make ci    # Run lint + shellcheck + 616 tests before submitting a PR

Credits

Architecture inspired by Claude Code Game Studios by Donchitos.

License

MIT