{
  "id": 15538084,
  "title": "Characterising and Analysing Retrieval-Augmented Generation Techniques",
  "url": "https://urgent.news/2026/10/16/characterising-and-analysing-retrieval-augmented-generation-techniques",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-16T09:55:31.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.23226v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "The advent of Large Language Models (LLMs) across many application domains has increased the need for Retrieval-Augmented Generation (RAG) to reduce hallucinations and improve LLM performance. Thus far, several RAG techniques with impressive features and capabilities have been proposed in the literature, making it a herculean task to select the most appropriate RAG techniques for specific problem…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}