Tutorials Prompt Engineering Tutorial
Introduction to RAG — Complete Guide
Introduction to RAG — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 41 of 100
RAG
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 5: RAG Systems
What is this?
RAG (Retrieval-Augmented Generation) searches your documents first, injects the best chunks into the prompt, then asks the LLM to answer from that context.
Why should you care?
PromptVerse Knowledge Search is built on RAG — answers cite internal docs instead of model memory.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
query = "What is the data retention period?"
chunks = vectorSearch(query, topK=4)
prompt = "Context:\n" + chunks.join("\n---\n") + "\n\nQ: " + query + "\nAnswer with [doc_id] citations."
answer = llm(prompt)What happened?
- vectorSearch returns relevant passages.
- Prompt wraps them with citation rule.
- Model composes answer grounded in chunks.
Practice next
- Pick 3 markdown files as fake KB.
- Ask 2 answerable questions.
- Manually paste chunks into prompt.
- Add "say unknown if context empty".
- Compare topK 3 vs 8.
Remember
Retrieve before generate. Citations prove grounding. Update docs without retraining.
Policy bot launch
HR enables leave-policy Q&A.
Outcome: RAG answers from handbook PDF chunks only.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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