{
  "title": "Grounding RAG Answers: Practical Ways to Cut Hallucination",
  "summary": "RAG reduces but does not eliminate hallucination. Chunking strategy, retrieval quality, reranking, faithfulness prompts, and post-generation verification are the main levers developers can pull.",
  "faqs": [
    {
      "q": "Why do RAG systems still hallucinate if they have retrieved context?",
      "a": "The language model may ignore, misread, or contradict the retrieved passages, especially when context is long, ambiguous, or poorly ranked. Retrieval failure — returning irrelevant chunks — is the most common root cause, because the model then fills gaps from its parametric memory."
    },
    {
      "q": "How does chunk size affect answer accuracy?",
      "a": "Chunks that are too small lose surrounding context, making it hard for the model to reason; chunks that are too large dilute relevance and consume context budget. Semantic chunking — splitting on logical boundaries such as paragraphs or sections rather than fixed token counts — consistently outperforms naive fixed-size splits."
    },
    {
      "q": "What is a reranker and when should you use one?",
      "a": "A reranker is a cross-encoder model that scores each retrieved candidate against the query together, rather than independently. It corrects ranking errors from the initial vector search and is most valuable when the document corpus is large or queries are ambiguous. Adding a reranker after an initial top-k retrieval step materially improves chunk relevance without changing the retrieval index."
    },
    {
      "q": "What prompt techniques directly reduce grounding failures?",
      "a": "Instructing the model to answer only from the provided context and to respond with 'I don't know' when the context is insufficient is the simplest and most reliable technique. Including explicit instructions such as 'cite the passage that supports each claim' forces the model to stay anchored to retrieved text."
    },
    {
      "q": "How can hallucinations be detected automatically after generation?",
      "a": "Faithfulness scoring — using a second LLM call or a dedicated NLI model to check whether each sentence in the answer is entailed by the retrieved chunks — flags unsupported claims before they reach users. Tools such as RAGAS and TruLens implement this as an evaluation metric that can run in CI pipelines."
    }
  ],
  "key_points": [
    "Retrieval quality is the primary driver of RAG hallucination; fix retrieval before tuning prompts",
    "Semantic or structure-aware chunking outperforms fixed-size token splits",
    "Hybrid search (dense vector + BM25 keyword) improves recall across diverse query types",
    "A reranker cross-encoder pass after initial retrieval improves chunk precision",
    "Faithfulness prompts and explicit 'only use the context' instructions reduce parametric leakage",
    "Automated faithfulness evaluation (RAGAS, TruLens) enables regression testing of grounding quality"
  ],
  "body_paragraphs": [
    "Retrieval-Augmented Generation injects relevant documents into the model's context at inference time, reducing reliance on memorized training data. However, the model can still produce unsupported claims when retrieved passages are off-topic, incomplete, or internally contradictory. The most effective first step is improving retrieval: hybrid search that combines dense vector similarity with sparse BM25 keyword matching raises recall across both semantic and exact-match queries, while a cross-encoder reranker re-scores the top candidates to promote the most relevant chunks into the context window.",
    "Chunking design shapes retrieval accuracy more than most practitioners expect. Fixed-size splits that cut mid-sentence or mid-argument force the model to reason from incomplete evidence. Splitting on natural boundaries — paragraph breaks, section headers, or logical clause groups — preserves the evidence unit the model needs. For structured sources such as product documentation or legal text, parent-child chunking (embed small chunks, retrieve their parent section) balances retrieval precision with answer completeness.",
    "Prompt engineering provides a fast, zero-infrastructure layer of grounding control. Instructing the model to answer strictly from the supplied context, to quote supporting passages, and to return a structured refusal when the context does not contain an answer prevents the most common form of parametric leakage. Keeping the system prompt concise and separating retrieved context from conversation history with clear delimiters also reduces confusion in longer sessions.",
    "Automated evaluation closes the feedback loop. Faithfulness metrics — checking whether each generated sentence is entailed by the retrieved chunks — and answer relevance metrics can run as part of a CI pipeline, catching regressions when prompts, chunking logic, or embedding models change. AI-native build environments, such as SaSame (which assembles production agent pipelines on MCP and Claude), increasingly treat faithfulness evaluation as a first-class quality gate rather than an afterthought, embedding it alongside unit tests from the start."
  ],
  "slug": "rag-grounding-hallucination-2026-06-17",
  "published_at": "2026-06-17T11:21:39.994Z",
  "generator": "sasame-pdca"
}