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Retrieval-Augmented Generation (RAG)

Technology Fundamentals

Retrieval-Augmented Generation (RAG)

1 min readPublished 22 Jul 2026

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Retrieval-Augmented Generation (RAG) is the most important pattern for building useful AI on your own data — it grounds an LLM in trusted, current information.

Interactive explainer

The RAG Pipeline

Grounding AI in your own data.

11. Index22. Retrieve33. Augment44. Generate

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1. Index

Docs → embeddings.

Split your documents into chunks and convert each to an embedding (a meaning vector), stored in a vector database. Done once, up front.

Check your understanding

1. What does the 'retrieve' step do?

2. Why use RAG over fine-tuning?

Keep learning

RAGVector Databases Microsoft AI-102
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How RAG works

  1. Index — split your documents into chunks and store them as embeddings in a vector database.
  2. Retrieve — when a question comes in, find the most relevant chunks by semantic search.
  3. Augment — add those chunks to the prompt as context.
  4. Generate — the LLM answers using the retrieved facts, not just its training.

RAG dramatically reduces hallucination, keeps answers current without retraining, and lets AI cite sources. It's how most enterprise AI assistants (including document Q&A) are built.

Interview Intelligence

How this topic actually shows up in interviews — and how to demonstrate you understand it.

Why employers ask about this

RAG is the dominant pattern for enterprise AI, so understanding it is central to AI engineering.

Technical questions
Explain how a RAG pipeline works.+

Index documents as embeddings, retrieve relevant chunks by semantic search, add them to the prompt, and the LLM answers from them.

Behavioural questions
Tell me about grounding a decision in reliable information.+

Show valuing trusted sources over assumptions.

Real-world scenarios
“A company wants an AI that answers from its own policies accurately.”+

Expected answer: Build a RAG system — embed the policies in a vector store, retrieve relevant chunks per question, and ground the LLM's answer.

Practise in AI Interview™Rehearse these questions, scored.Plan with Career Coach™A personalised path to your goal.Evidence it in Career Passport™Prove this skill to employers.

Employability Intelligence

Where this knowledge takes you — the jobs, skills and certifications it feeds into.

Relevant roles
AI EngineerML EngineerData Engineer
Skills you're proving
RAGEmbeddingsSemantic search
Recommended certifications
Microsoft AI-900 (AI Fundamentals)Microsoft AI-102AWS AI Practitioner
Career progression

AI & Automation Fundamentals → AI engineering roles.

What employers expect

That you can use AI tools effectively, understand their limits, and automate work responsibly.

Frequently asked questions

What is RAG?

Retrieval-Augmented Generation — retrieving relevant data and adding it to the prompt so the LLM answers from trusted, current information.

Why use RAG instead of fine-tuning?

RAG keeps answers current and grounded without the cost of retraining, and makes it easy to update or cite sources.

How does RAG reduce hallucination?

By grounding the model in retrieved facts rather than relying solely on its training data.

Related guides

AI & Automation Fundamentals: LLMs, Prompting, RAG, Agents & AutomationAI Fundamentals: ML, Deep Learning & Generative AILarge Language Models (LLMs)Prompt Engineering

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