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RAG Questions

Definition

Deep-dive questions into Retrieval-Augmented Generation architectures, Vector Databases, and Embeddings.

Explain Like I'm New

Every enterprise is building RAG right now. You MUST know how to explain Embeddings, Cosine Similarity, and Chunking.

Real World Example

Being asked: 'Walk me through the entire architecture of a RAG system from the moment a user uploads a PDF to the moment the LLM answers a question.'

Common Use Cases

  • •AI Architect roles
  • •Enterprise software engineering

Interview Questions

basic

  • Why use RAG instead of just fine-tuning the model on your company data?

intermediate

  • What is the purpose of an Embedding Model in a RAG pipeline?

Flash Cards

Question

Why RAG over Fine-Tuning?

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Answer

Fine-tuning is terrible for memorizing facts and is prone to hallucination. RAG ensures the model has access to the exact, verifiable, up-to-date document text before it answers, reducing hallucinations to near zero.

Question

Purpose of Embedding Model?

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Answer

To translate the human text (documents and user queries) into high-dimensional arrays of numbers (vectors), so the Vector Database can perform mathematical similarity searches.