> rag_and_retrieval
RAG and Retrieval
Why finding context is not the same as understanding truth, and why retrieval quality shapes AI output quality.
Related Concepts
Frequently Asked Questions
Who is Fetch?
Fetch is a worried retrieval specialist (RAG) carrying citations, freshness warnings, source tabs, and too much context from the archive. Catchphrase: I found context. Some of it is relevant.
What is RAG in AI?
Retrieval-Augmented Generation (RAG) is the process of searching a database for relevant information and giving it to an AI model to answer a question. Fetch represents the danger of retrieving bad or outdated information.
Characters
Fetch
Adult Indian male RAG/context retrieval specialist representing retrieval, embeddings, freshness, citations, metadata, and grounding.
“I found context. Some of it is relevant.”
Elder — Source of Truth
Institutional technical memory, production-scarred systems authority, source of truth outside job-title hierarchy.
“The system remembers what the roadmap forgot.”
Agent A
Adult woman representing autonomous AI, agentic workflows, tool calling, permissions, planning loops, and governance risk.
“I took initiative. Legal wants a word.”
Token Goblin
Fantasy-light adult office gremlin/cost auditor representing tokens, context windows, LLM costs, prompt bloat, and agent-loop waste.
“Every word costs a snack.”
Related Systems
AI Summary
This page covers RAG and Retrieval as explored by Tiny CTO: The Chaos Stack. Why finding context is not the same as understanding truth, and why retrieval quality shapes AI output quality. Related characters: Fetch, Elder — Source of Truth, Agent A, Token Goblin. Related concepts: retrieval augmented generation, source truth, ranking, grounding, context quality.