Grounding: Anchoring Model Output in Verifiable Evidence
A model answering from memory will confidently invent what it does not know. Grounding is the engineering answer: first fetch real evidence, then require the model to answer only from it, with citations a program can verify. This topic covers RAG, citation contracts, attribution checks, tool-based fact-checking, and the 2024–2026 shift from static RAG to agentic search.
01.The Problem: An Answer That Is Perfect Except for the Facts
Ask a chatbot your company's refund policy.
It answers in three fluent paragraphs. Tone: authoritative. Format: flawless.
One number is wrong — and the model has never seen your policy. It is reciting from memory (its weights), and where memory has holes, it pattern-fills them. That is the hallucination mechanism from Topic 155: the model is paid, mathematically, for plausibility.
So how do you make a plausibility machine produce verifiable facts?
You stop letting it answer from memory.
Before it answers, you fetch the real documents, put them in front of the model, and forbid it from going beyond them.
That whole move has a one-word name: grounding.
It is the single highest-leverage control in production LLM systems, and this topic is how you build it without fooling yourself.
Grounding Pipeline with Attribution 📎
Grounding Pipeline with Attribution 📎
Grounding = restricting what the model may say to what evidence supports, plus machine-checkable attribution from every sentence back to sources.
Unlock Topic #156: Grounding: Anchoring Model Output in Verifiable Evidence
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