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RAG for Beginners (No Code): Make AI Answer From Your Files
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RAG for Beginners (No Code): Make AI Answer From Your Files

Learn chunking, embeddings and vector search visually, and get AI answers from your own files with sources. No code.
Created byAdi Tejus
Last updated 10/2026
English

What you'll learn

  • Explain why AI models can't see your files by default
  • Understand RAG as an open-book exam for AI
  • Know how documents are chunked and turned into embeddings
  • Understand how vector search finds what you meant
  • Get answers with their sources attached so you can check them
  • Diagnose why RAG still gets things wrong, and apply the fixes

Course content

6 sections • 18 lectures • 1h 3m total length
  • Why AI Can't See Your Files4:24
  • Hallucination, in plain words2:40
  • Common myths and mistakes, and the three-question test4:00
  • Quick check: Why AI Can't See Your Files

Requirements

  • Basic idea of what a chat assistant is
  • No coding or maths required

Description

This course contains the use of artificial intelligence.

Complete RAG course for beginners: make AI answer from your own documents. A RAG course (retrieval-augmented generation) with no maths and no code.

Why AI can't see your files, how RAG works like an open-book exam, how documents are chunked and turned into embeddings, how vector search finds what you meant, how to get answers with their sources attached, and why RAG still gets things wrong, with the fixes.

What's inside:

  • Why AI Can't See Your Files: the wrong picture in our heads, the brilliant new hire with no handbook, why it won't just say it doesn't know, the one way in: the prompt, the three-question test.
  • How AI Answers From Your Documents: why you can't paste ninety pages, the open-book exam, generate the answer from them, show the source line, the one question to ask any helper.
  • How Your Documents Become Searchable: why not hand over the whole handbook, different words, same meaning, pinning chunks on the map of meaning, the index, built before any question, the right-size test for chunks.
  • Search That Finds What You Meant: keyword search: matching the letters, when the words match but the meaning doesn't, vector search: find the nearest dots, no shared words, still the right chunk, the two-second test for a search box.
  • Get AI Answers You Can Actually Check: finding the pages isn't answering, the exam with the teacher's rule, instruction three: name the source, why a source line builds trust, the one test for a document answer.
  • Why Your AI Still Gets It Wrong: tuning a library, failure one: the page was never found, when RAG is the wrong tool, the two-column test sheet.

How it's taught: 17 short lectures of a few minutes each, with visual explanations and everyday examples. No maths and no code, one clear idea at a time, and every section ends with a recap and a common-mistakes check. The narration uses an AI voice.

Resources: a one-page cheat sheet for each section is in the lecture Resources. (The narration sometimes says the cheat sheet is "in the description"; on Udemy it's in Resources.)

Who this course is for:

  • Beginners who want AI to answer from their own documents
  • Students and professionals exploring AI for work
  • Developers planning their first RAG chatbot