> rag_&_retrieval
RAG & Retrieval
RAG (Retrieval Augmented Generation) grounds AI responses in real data — but retrieval quality, context freshness, and relevance are never guaranteed. Fetch carries the citations.
Related Concepts
Frequently Asked Questions
Who is Fetch?
Fetch is a worried retrieval specialist carrying citations, freshness warnings, source tabs, and too much context from the archive. Catchphrase: I found context. Some of it is relevant.
What does The Chaos Stack say about RAG?
The Chaos Stack shows that RAG retrieval adds a new layer of predictable chaos: context may be stale, irrelevant, or overwhelming. Fetch personifies the anxious reality of grounding AI in imperfect data.
This page covers RAG & Retrieval as explored by Tiny CTO: The Chaos Stack. RAG (Retrieval Augmented Generation) grounds AI responses in real data — but retrieval quality, context freshness, and relevance are never guaranteed. Fetch carries the citations. Related characters: Fetch, Agent A, Token Goblin. Related concepts: retrieval augmented generation, vector search, grounding, context freshness, citations, embeddings.