Question 1

What is retrieval-augmented generation in plain English?

Accepted Answer

A pattern where an LLM looks up relevant text from your own data store before answering a question, instead of relying on its training data. The model is conditioned on the retrieved passages, which dramatically reduces hallucination on domain-specific questions.

Question 2

When should you use graph RAG instead of vector RAG?

Accepted Answer

When the answer requires multi-hop reasoning across explicit relationships — customer→account→contract, or component→supplier→country. Pure vector search finds semantically similar text but misses structural relationships. Graph RAG combines both.

Question 3

What's the simplest RAG stack for a small team?

Accepted Answer

A managed vector database (Pinecone, Weaviate, or pgvector on Postgres), an embedding model (OpenAI text-embedding-3 or open-source bge-large), and a retrieval-then-prompt wrapper around your existing LLM call. You can ship a working v1 in a single afternoon.

Question 4

How do you evaluate a RAG system?

Accepted Answer

Three layers: retrieval precision/recall (did we surface the right passages?), faithfulness (does the answer match the retrieved text?), and answer quality (is the final response useful?). Tools like Ragas and TruLens automate this end-to-end.

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Frequently asked questions

What is retrieval-augmented generation in plain English?

When should you use graph RAG instead of vector RAG?

What's the simplest RAG stack for a small team?

How do you evaluate a RAG system?

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