Skill
Embeddings & RAG
Retrieve then generate. Garbage retrieval is garbage generation.
Do these first
What you will be able to do
- Explain cosine similarity of embeddings
- Chunk documents on purpose
- Cite retrieved context in the answer
Curated resources
Start with the pick, then branch out
Scores are LearnPath editorial opinions (clarity, cost, freshness, project density) — not an objective ranking. Every YouTube item is embedded officially after a click-to-load facade.
Official docs
LearnPath pickinteractiveintermediate
Hugging Face NLP course
Hugging Face8h
Free. Chapters 1–4 cover the stack you will actually use.
Score 94
Open
10 minFastest pathQuick pathyoutube videobeginner
What is a Vector Database? Powering Semantic Search & AI Applications
IBM Technology10 min
Ten minutes that explain what a vector database does, how similarity search works and when a library like FAISS is enough instead. The right orientation before choosing infrastructure.
Score 69
Watch
1h 44mProject-basedProject-orientedyoutube videointermediate
Production-Grade AI Project Tutorial – Build & Deploy
freeCodeCamp.org1h 44m
A 2025 freeCodeCamp build that takes an AI feature all the way to a deployed service — model calls, API layer, container, deployment. The clearest demonstration of the difference between a notebook demo and a production AI application.
Score 82
Watch
2h 33mProject-basedMore curatedyoutube courseintermediate
Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer
freeCodeCamp.org2h 33m
Two and a half hours from a LangChain engineer that go well past naive RAG — query rewriting, routing, multi-index retrieval, re-ranking — with runnable notebooks. The technical depth layer after a first RAG course.
Score 82
Watch
36 minLearnPath pickMore curatedyoutube videointermediate
Vector Embeddings Tutorial – Code Your Own AI Assistant with GPT-4 API + LangChain + NLP
freeCodeCamp.org36 min
Thirty-six minutes that build a working question-answering assistant on embeddings, including the chunking and similarity decisions that make or break results. Concrete enough to reuse the architecture immediately.
Score 81
Watch
59 minBest for beginnersMore curatedyoutube videobeginner
RAG Crash Course for Beginners
KodeKloud59 min
Fifty-nine minutes published September 2025 that walk the full RAG loop — ingest, chunk, embed, retrieve, generate, evaluate — with the failure modes named as they appear. The most current short RAG course verified.
Score 80
Watch8 videos
More curatedplaylistintermediate
Complete RAG Playlist
Krish Naik4h 11m
Eight videos covering RAG end to end with LangChain and vector stores — a compact playlist for learners who prefer many short sessions to one long video.
Score 72
Open