{
  "title": "RAG — Retrieval-Augmented Generation",
  "description": "Emma and Ryan dig into Retrieval-Augmented Generation — how it works under the hood, why LLMs hallucinate without it, vector databases, chunking strategies, re-ranking, and where RAG falls flat. From naive RAG to agentic RAG, they cover the full landscape with real production gotchas.",
  "topic": "RAG, Retrieval-Augmented Generation",
  "date": "2026-04-18",
  "voice_map": {"HOST_A": "en-US-EmmaNeural", "HOST_B": "en-GB-RyanNeural"}
}