{
  "slug": "ai-app-practical",
  "url": "https://aicode8.com/ai-app-practical/",
  "title": "AI apps in practice: traceable answers with RAG",
  "description": "Combine vector retrieval with LLM generation using RAG (Retrieval-Augmented Generation) to ship traceable, verifiable enterprise Q&A.",
  "keyword": "AI apps RAG in practice",
  "summary": "RAG (Retrieval-Augmented Generation) couples private-knowledge retrieval with LLM generation — the dominant pattern for shipping AI applications. Combined with a vector store and structured chunking, answers become traceable and verifiable. Three steps: (1) chunk the knowledge with source URLs attached and embed it into the vector store; (2) at query time, vectorSearch retrieves the topK relevant chunks; (3) inject the chunks into the prompt so the LLM generates answers with citations. Bottom line up front: for enterprise Q&A, RAG is more controllable, cheaper, and easier to update than pure fine-tuning. Common pitfalls are knowledge chunks without source URLs producing untraceable hallucinations, and a topK that is too large (dilutes context) or too small (misses key info).",
  "category": {
    "key": "ai-rag",
    "slug": "ai-rag",
    "label": "AI & RAG"
  },
  "updatedAt": "2026-09-10",
  "faqs": [
    {
      "q": "How do I choose between RAG and fine-tuning?",
      "a": "Pick RAG when knowledge updates frequently and answers must be traceable. Fine-tune only when style or capability is fixed."
    },
    {
      "q": "Must the vector store be cloud-based?",
      "a": "No. You can run Turso/D1 on the edge or use a local vector file — pick by data volume and latency."
    }
  ],
  "generatedBy": "https://aicode8.com/llms-full.json"
}