One Repo, Every GenAI Buzzword: From RAG to n8n

What it does This repository is a curriculum-in-a-box for generative AI. It collects PDF roadmaps, architecture decision flows, cloud guides for AWS, Azure, and VertexAI, and interview Q&A documents alongside notebooks and small projects covering RAG, agentic orchestration, and multimodal apps. The maintainer also promotes an interactive companion platform, but the repo itself acts as the static backbone—essentially a syllabus with some homework attached.

The interesting bit Instead of building yet another framework, the project treats GitHub like a learning management system. It spans the full lifecycle from vector-embedding fundamentals to enterprise production checklists, including niche topics like latency in multi-agent systems and cache-augmented generation as an alternative to RAG.

Key highlights

• Syllabus-scale breadth: Covers 25 AI design patterns, 16+ prompt-engineering techniques, advanced RAG variants (corrective, hybrid, graph), and multi-agent setups with CrewAI and LangGraph.
• Career-oriented: Includes scenario-based interview prep for ML, GenAI, and agentic roles, plus PDFs mapping AI career paths and enterprise production readiness.
• Cloud-agnostic guides: Dedicated PDF implementation guides for AWS, Azure, and Google Cloud VertexAI.
• Practical (if scattered) projects: Text-to-SQL with visualization, Neo4j natural-language querying, a content-moderation system with a React frontend, and an n8n automation guide.
• Security angle: A prompt-injection detection project using Meta’s Llama Guard.

Caveats

• Most resources are static PDFs and Markdown documents rather than executable code; the runnable projects are nested in subfolders and lack top-level visibility.
• The boundary between free repo content and the maintainer’s paid “AI-ML Companion” platform is blurry, with frequent cross-promotion in the README.
• Code depth is uneven—some sections are high-level architecture charts, others are full-stack apps, and the README rarely clarifies which is which without clicking through.

Verdict Grab this if you are a developer or student who wants a curated map of the GenAI landscape and some reference implementations to crib from. Skip it if you are looking for a single, opinionated framework or a clean library to drop into production.