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Grounding

Definition

Grounding is the practice of connecting AI model outputs to external, verified sources of information to ensure that generated content is factually accurate, up-to-date, and traceable to authoritative references. Rather than relying solely on knowledge encoded in the model's parameters during training, grounded AI systems actively retrieve and reference external data when generating responses.

The most common grounding technique is retrieval-augmented generation (RAG), where relevant documents are retrieved from a knowledge base and provided as context to the model before it generates a response. Other grounding approaches include function calling to query live databases, web search integration, and citation generation that links claims to specific source documents. Effective grounding transforms AI from a confident but unreliable narrator into a system that shows its work.

Why It Matters for Product Managers

Grounding is the primary defense against hallucination, the single biggest barrier to building AI features that users trust for consequential decisions. An AI assistant that occasionally fabricates information is a toy. An AI assistant that consistently references real data and can show where its answers came from is a tool users will rely on daily.

For product managers, grounding decisions directly shape the user experience and the product's value proposition. How much context to retrieve, which sources to trust, whether to show citations, and how to handle cases where no grounding information is available are all product decisions with significant impact on quality, cost, and user trust. PMs building AI features for domains where accuracy matters -- enterprise, healthcare, finance, legal -- must treat grounding as a core architectural requirement, not an optional enhancement.

How It Works in Practice

  • Build a knowledge base -- Curate and index the authoritative sources your AI should reference: product documentation, knowledge base articles, database records, or verified datasets. Structure this data for efficient retrieval.
  • Implement retrieval -- Set up a retrieval pipeline using vector search, keyword search, or hybrid approaches to find the most relevant documents for each user query.
  • Provide context to the model -- Include retrieved documents in the model's prompt context, with clear instructions to base its response on the provided sources and acknowledge when information is not available.
  • Generate citations -- Configure the model to reference specific sources in its responses, allowing users to verify claims and building trust through transparency.
  • Monitor grounding quality -- Track metrics like citation accuracy, source relevance, and hallucination rates in production to continuously improve the grounding pipeline.

    • Common Pitfalls

  • Retrieving irrelevant documents that confuse the model rather than grounding it, leading to responses that cite sources but still contain inaccuracies.
  • Building a knowledge base that becomes stale, so the AI is grounded to outdated information that may be worse than the model's parametric knowledge.
  • Over-constraining the model to only use retrieved information, preventing it from making useful inferences or connections that the source documents do not explicitly state.
  • Not implementing citation verification, so the model appears grounded but actually generates plausible-looking citations that do not match the source content.

    • Related Concepts

    Grounding is the primary solution to Hallucination in AI systems. Retrieval-Augmented Generation (RAG) is the most common grounding architecture, using Vector Databases for efficient document retrieval. Function Calling provides real-time grounding by querying live data sources, and AI Evaluation (Evals) measures grounding effectiveness through factual accuracy metrics.

    Related Terms

    HallucinationRetrieval-Augmented Generation (RAG)Function CallingVector DatabaseAI Evaluation (Evals)

    Frequently Asked Questions

    What is grounding in product management?+
    Grounding in product management refers to techniques that anchor AI-generated content to verified sources of truth. When an AI feature cites your product documentation, pulls from your knowledge base, or references real database records instead of generating plausible-sounding fiction, it is grounded. This is essential for any AI feature where factual accuracy matters.
    Why is grounding important for product teams?+
    Grounding is important because ungrounded AI confidently generates plausible but incorrect information, which destroys user trust and can cause real harm. Product teams that implement grounding techniques can build AI features users actually rely on for decisions, because responses are backed by verifiable sources rather than the model's training data alone.
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