MC07: Hugging face Ecosystem and Intro to Retrival Augemented Generation

## MC07 (AI Residency Cohort 9)

MC07: Hugging Face Ecosystem + Introduction to Retrieval-Augmented Generation (RAG)

Part of AI Residency — Join Cohort 10

If you want to build AI apps that answer with your data (not guesses), RAG is the foundation. In this masterclass, we’ll first map the Hugging Face ecosystem (Models, Datasets, Spaces, Inference, Transformers, the Hub), then break down RAG (Retrieval-Augmented Generation)—the most practical pattern for building document-grounded assistants for real business use cases.

### What you’ll learn

• How Hugging Face fits together for builders: Hub → Models → Datasets → Spaces → Inference
• Why RAG matters: reduce hallucinations, keep answers fresh, and support citations
• The RAG pipeline: docs → chunking → embeddings → vector search → prompt → generation
• RAG architecture patterns (high level): where each component lives and how data flows
• Best practices + common pitfalls: chunk sizing, overlap, retrieval quality, evaluation basics

### Outcome

You’ll leave with a clear blueprint to start building your first RAG assistant for PDFs, FAQs, policies, internal docs, or course content—and a roadmap for what comes next (reranking, hybrid search, eval, and production deployment).

This masterclass is part of the AI Residency.

✅ Join New AI Residency — Cohort 10 to access this session and the full learning path Special scholarship available.
https://academy.decodingdatascience.com/airesidencyfasttrack