Retrieval-augmented generation

How an application retrieves external evidence at request time and hands it to a model to produce a grounded, citable answer — composing semantic search and chunking into a full pipeline, and knowing exactly where that pipeline tends to break.

Questions in this set

13 questions · 21 min read

01What is retrieval-augmented generation (RAG)?
02What problem does RAG solve?
03What is the basic RAG pipeline?
04Can we build the smallest RAG example?
05How is RAG different from long-context prompting?
06How is RAG different from fine-tuning?
07What should retrieved context contain?
08How should the generation prompt use evidence?
09Where can a RAG system fail?
10How do keyword, vector, and hybrid retrieval differ?
11What is reranking?
12How do you secure retrieved content?
13When is RAG unnecessary?

Tutor

Want me to explain it differently?

AI concepts can be dense. Tell me what's confusing and I'll find a new analogy.

Retrieval-augmented generation

Questions in this set

13 questions · 21 min read

01What is retrieval-augmented generation (RAG)?
02What problem does RAG solve?
03What is the basic RAG pipeline?
04Can we build the smallest RAG example?
05How is RAG different from long-context prompting?
06How is RAG different from fine-tuning?
07What should retrieved context contain?
08How should the generation prompt use evidence?
09Where can a RAG system fail?
10How do keyword, vector, and hybrid retrieval differ?
11What is reranking?
12How do you secure retrieved content?
13When is RAG unnecessary?

Tutor

Want me to explain it differently?

AI concepts can be dense. Tell me what's confusing and I'll find a new analogy.