Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers

Bashiri Smith · Facebook reel · 2026-10-01 · 0:55 · 12,428 views · Open on Facebook

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

Summary

A quick walkthrough of a basic Retrieval-Augmented Generation (RAG) pipeline that lets an LLM answer questions using private data. It covers each stage, from extracting and cleaning documents, chunking, embedding and storage through to similarity search and answer generation. It also names a specific tool for each step: Unstructured, LangChain text splitters, Cohere embeddings, and Pinecone or Weaviate.

Key points

• RAG finds relevant information in your own data and gives it to an LLM so it can answer a question, which lets the LLM work with private data.
• Step 1, prepare data: start with sources such as PDFs or company documents.
• Step 2, extract and clean: use Unstructured to pull out the text and fix broken formatting.
• Step 3, chunk: use LangChain's text splitters to break documents into smaller, searchable passages.
• Step 4, embed: pass each chunk to an embedding model (e.g., Cohere), which turns it into a list of numbers that represents its meaning.
• Step 5, store: save the embeddings alongside the original chunk text in a vector database such as Pinecone or Weaviate.
• Step 6, search: embed the user's question with the SAME embedding model and find stored chunks with similar meaning.
• Step 7, generate: send the retrieved chunks plus the original question to the LLM so it can generate a grounded answer.

Resources mentioned

• Unstructured · tool · unstructured.io · free
  Document and data processing for building RAG over your domain documents.
• LangChain · tool · github.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Open-source framework with ready-made integrations and common interfaces for connecting LLMs, embedding models, vector stores and tools.
  Also in: SWE-to-AI Engineer Plan for 2027: LLMs, RAG, Agents, Evals, Job Search (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), LangChain vs. LangGraph Explained with One RAG Chatbot (Bashiri Smith on Facebook · notes), How to Relearn LLMs & RAG in 2026: A 7-Step Roadmap with Free Resources (Bashiri Smith on Facebook · notes) and 3 more
• Cohere · person · x.com · free · recommended by both Bashiri Smith & Melvin Vivas
  AI company that builds language, embedding and reranking models, and now the Transcribe speech-recognition model.
  Also in: Cohere Parse Beats Frontier LLMs at Receipt Parsing (Melvin Vivas on X · notes), Cohere Parse: Pricing vs Parse Bench Score (Melvin Vivas on X · notes), Cohere's North Micro Vision: a small open-source vision model for documents (Melvin Vivas on X · notes), Local Audio Transcription with Cohere Transcribe on WebGPU (Melvin Vivas on X · notes) and 1 more
• Pinecone · tool · pinecone.io · free
  A managed, production vector database (free tier).
  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), How RAG Works Under the Hood: Chunking, Embedding, Storage, 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: 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), How RAG Works Under the Hood: Chunking, Embedding, Storage, Retrieval (Bashiri Smith on Facebook · notes), Why RAG Alone Won't Make You a Lasting AI Engineer (Bashiri Smith on Facebook · notes)
• RAG-Expert-Guide-BASWE (RAG cheat sheet) · pdf · drive.google.com · free
  The creator's full RAG cheat sheet, shared as a Google Drive document.
• 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: 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), 5 Things AI Engineers Must Evaluate: RAG, Agents, Models, Data, Guardrails (Bashiri Smith on Facebook · notes) and 76 more

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

• Download the full RAG cheat sheet from the Google Drive link (or comment "RAG" on the reel to have it sent to you).
• Join the BASWE.Ai Engineer community on Skool if you want help from working AI engineers and recruiters.
• Embed user questions with the same embedding model you used for your document chunks.
• Build a basic RAG system over your own PDFs or company documents: Unstructured for extraction, LangChain text splitters for chunking, Cohere for embeddings, Pinecone or Weaviate for storage, then an LLM to generate answers.