Tutorials AI Fundamentals Tutorial
RAG Introduction
RAG Introduction: free step-by-step lesson with examples, common mistakes, and interview tips — part of AI Fundamentals Tutorial on Toolliyo Academy.
On this page
AI Fundamentals Tutorial · Lesson 37 of 120
RAG Introduction
Foundations & ML ✓ → DL, LLM & NLP → Build & Safety → Projects
DL, LLM & NLP · 2 — Language & vision · ~6 min · Generative AI & LLMs
What is this?
RAG (Retrieval-Augmented Generation) means: search your documents first, put the best chunks into the prompt, then ask the LLM to answer using that context.
Why should you care?
Company FAQs, policies, and product docs should not be “memorized” by a base model — RAG keeps answers grounded and updatable.
See it live — copy this example
Treat examples as Python-style notes you can paste into a notebook or rewrite in your stack. Prefer public sample data — never real private records.
# RAG loop
question = "What is our refund window?"
chunks = search_knowledge_base(question, top_k=3)
prompt = f"Use only this context:\n{chunks}\n\nQuestion: {question}"
answer = llm(prompt)What happened?
- search_knowledge_base returns relevant text.
- The prompt forbids using outside knowledge.
- The LLM writes the final answer from chunks.
Practice next
- Pick one PDF or markdown file as a fake knowledge base.
- Write 3 questions it can answer.
- Manually copy two paragraphs into a prompt and ask a model (or imagine the answer).
- Force “quote the source title”.
- Return “I don’t know” when chunks are empty.
Remember
Retrieve → then generate. Chunks beat whole files. Update docs without retraining the LLM.
Policy bot
HR asks leave-policy questions.
Outcome: Answers come from the handbook chunks, not guesswork.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
Sign in to ask a question or upvote helpful answers.
No questions yet — be the first to ask!