How RAG Works Under the Hood: Chunking, Embedding, Storage, Retrieval

Bashiri Smith · Facebook reel · 2026-09-13 · 1:12 · 19,473 views · Open on Facebook

Topics: Retrieval-Augmented Generation (RAG), Embeddings & Vector Databases · Level: beginner

Summary

The creator calls RAG one of the most important AI engineering skills right now. The caption says it is explicitly requested in nearly 40% of AI engineer job postings. The video breaks a basic RAG pipeline into four steps: chunk documents at semantic boundaries with overlap, embed the chunks into vectors, store them in a vector database, then embed the user's question, retrieve the closest chunks and pass them to an LLM as context. It ends by pointing out that production-grade RAG goes much deeper, and it shares a free RAG guide.

Key points

• Caption claim: RAG is explicitly requested in nearly 40% of AI engineer job postings.
• Step 1, chunking: split source documents (PDFs, internal wikis, webpages, Slack messages, etc.) into chunks. Don't split randomly. Split at semantic boundaries like paragraphs, sections and headers.
• Leave overlap between chunks so a retrieved chunk keeps its surrounding context.
• Step 2, embedding: run each chunk through an embedding model, which turns the text into a high-dimensional vector (a list of numbers that represents its meaning). Chunks with similar meaning end up closer together.
• Step 3, storage: save the vectors in a vector database such as Pinecone, Weaviate or pgvector.
• Step 4, retrieval and generation: embed the user's question with the same embedding model, find the closest matching vectors, pull the original text of those chunks, and give it to the LLM as context.
• The LLM then answers using the relevant retrieved context instead of only its training data.
• Basic RAG is fairly simple, but production-level RAG is 'a completely different beast' and goes much deeper.

Resources mentioned

• BASWE.Ai Engineer (Skool community) · community · skool.com · paid
  The creator's paid community and program, with an AI learning roadmap (including the full ops and evaluation track), daily calls with engineers and recruiters, resume and portfolio help, and a job-search pipeline.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes) and 76 more
• Pinecone · tool · pinecone.io · free
  A managed, production vector database (free tier).
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How RAG Works: Chunking, Embedding, Vector Storage, and Retrieval (Bashiri Smith on Facebook · notes), Why RAG Alone Won't Make You a Lasting AI Engineer (Bashiri Smith on Facebook · notes)
• Weaviate · tool · weaviate.io · free
  An open-source vector database for storing embeddings and running semantic and hybrid search.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How RAG Works: Chunking, Embedding, Vector Storage, and Retrieval (Bashiri Smith on Facebook · notes), Why RAG Alone Won't Make You a Lasting AI Engineer (Bashiri Smith on Facebook · notes)
• pgvector · tool · github.com · free
  An open-source Postgres extension for storing embeddings and running vector similarity search.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How RAG Works: Chunking, Embedding, Vector Storage, and Retrieval (Bashiri Smith on Facebook · notes), 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (Bashiri Smith on Facebook · notes)

From the PDF shared here: How to become an expert in RAG (BASWE AI Engineer Field Guide)

Open the original · 1 pages

A one-page field guide from BASWE that lays out a 5-stage path to mastering retrieval-augmented generation. The stages are: get the mental model, build a base pipeline (chunking, embeddings, vector DB, retrieval), upgrade retrieval (hybrid search, reranking, RAG-Fusion, HyDE), ship it to production (evals, latency/cost, guardrails, monitoring), and explore advanced methods (agentic, knowledge-graph and multimodal RAG, RAPTOR, ColBERT, corrective RAG). It lists free courses and YouTube videos for each step and ends with a pitch for the creator's paid Skool community. Treat the stages as a checklist and, as the guide says, ship one real project at every stage.

• Class Central: 12 best RAG courses · article · classcentral.com · free
  Ranked and tested list of RAG courses; the fastest way to find the Boot.dev, Weights & Biases RAG++, Duke and Google Cloud picks.
• Complete RAG tutorial 2026, with free labs · video · youtube.com · free
  A full code-along crash course that goes from setup to a working RAG system.
• DeepLearning.AI: Retrieval Augmented Generation · course · learn.deeplearning.ai · paid
  Course taught by Zain Hasan with five hands-on modules. Module 1 is free to audit; the full course needs a paid upgrade. The same site hosts free short courses on agentic, knowledge-graph and multimodal RAG.
• LangChain: RAG From Scratch (YouTube playlist) · video · youtube.com · free
  Short-video series by Lance Martin that goes from indexing basics up to advanced routing and reranking; the guide calls it the gold standard.
• Learn Retrieval Augmented Generation (Boot.dev) · course · boot.dev · paid
  Boot.dev's RAG course (one of Class Central's picks): lessons are free to read, interactive exercises and certificates need a paid membership.
• RAG explained in 20 minutes · video · youtube.com · free
  A fast, clear intro with a hands-on project; the quickest way to see the full RAG loop in action.
• RAG++: From POC to Production (Weights & Biases) · course · wandb.ai · free
  RAG course from Weights & Biases named in the Class Central roundup of the best RAG courses.
• Turing Post: 10 RAG Courses in 2026 (Free, Trial, and Paid Options) · article · turingpost.com · free
  Agentic, multimodal and knowledge-graph RAG learning paths gathered in one place.

Try this

• Comment 'search' on the video to get the links to the free RAG resources.
• Read the free RAG-Expert-Guide-BASWE document to learn more about RAG, including production-level RAG.