{
  "id": 7533443,
  "title": "What Is RAG? Retrieval-Augmented Generation Explained",
  "url": "https://urgent.news/2026/09/15/what-is-rag-retrieval-augmented-generation-explained",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-15T10:49:17.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/amareswer/what-is-rag-retrieval-augmented-generation-explained-4afd"
  },
  "original_language": "en",
  "account": "Retrieval-Augmented Generation, or RAG, is a method that lets language models answer questions using documents they weren't originally trained on. It does this by searching those documents for relevant passages and providing the model with the results as context for its response, without the need for retraining or fine-tuning. This approach combines the model's pre-existing knowledge with a live lookup into an external index.\n\nRAG is a pattern, not a specific tool. It works by retrieving relevant information for a query and then generating an answer using that information as context. The original paper on RAG, authored by Patrick Lewis and colleagues at Facebook AI Research, paired a pre-trained sequence-to-sequence model with a dense vector index accessed by a pre-trained neural retriever.\n\nThe retrieval process involves chunking documents into smaller passages, embedding each chunk into a vector using an embedding model, and storing these vectors in a vector database. The incoming question is also embedded in the same way. The vectors of the chunks are then compared to the question vectors, and the chunks whose vectors are closest to the question are retrieved. These chunks are used as context to generate an answer.\n\nA vector database is a key component of RAG. It indexes by geometric closeness rather than exact values, allowing for searches that can match relevant information even when there's little word overlap. For instance, a search for \"how do I reset a password\" could match \"forgot your login credentials\" even with almost no word overlap.\n\nHowever, having more retrieved chunks doesn't necessarily mean better answers. Sometimes, the model's performance is highest when relevant information occurs at the beginning or end of the input context, and it significantly degrades when it's buried in the middle. Therefore, a tighter retrieval step of 3-5 genuinely relevant, well-ranked chunks often outperforms a looser one with more, worse-ranked results.\n\nRAG and fine-tuning are two different methods for adding new knowledge to language models. RAG adds new knowledge without touching the model's weights, making it suitable for data that changes. Fine-tuning, on the other hand, changes the model's behavior, tone, or output format by retraining the weights. Many production systems use both methods to solve different problems.\n\nA common mistake when implementing RAG is to chunk the documents without testing the retrieval quality. It's also important not to assume that the retriever always finds something relevant, as this doesn't stop hallucination, it just changes what gets hallucinated. Skipping re-indexing after data changes is another common mistake. Not every task is a RAG problem - if the answer already lives in the model's training data, it may not be necessary to use RAG.",
  "summary": "Originally published on DevToolHub . What is RAG, in one sentence? A way to make an LLM answer questions using documents it was never trained on, by searching those documents for relevant passages and handing the model the results as context before it generates a response. No retraining, no fine-tuning — just a search step bolted onto the front of a normal prompt. The term comes from a 2020…",
  "key_points": [
    "RAG combines pre-existing model knowledge with live lookup into external index",
    "Key component of RAG is vector database for indexing and retrieving relevant document chunks"
  ],
  "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."
}