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Grounding
定義
Grounding is the practice of tying an AI model's answers to verified source material — your documentation, live data, or knowledge base — so responses reflect real facts rather than the model's own guesses.
How it works
Instead of letting a model answer purely from its training, a grounded system supplies relevant, trusted context at query time — retrieved articles, database records, or tool results — and instructs the model to base its answer on that context, often citing it.
Grounding is the goal that techniques like retrieval-augmented generation and tool use are built to achieve: the model reasons over content it can point to, not just what it happens to remember.
Why it matters for support
Grounding is the primary defense against hallucination. A support answer grounded in your current help center and live account data is accurate and specific; an ungrounded one may sound confident while being wrong. The catch is that grounding is only as reliable as the sources behind it — clean, non-contradictory content produces trustworthy answers.
よくある質問
What is the difference between grounding and RAG?
Grounding is the outcome — answers tied to real sources. RAG is one common method for achieving it: retrieving relevant passages and passing them to the model. Tool use is another way to ground answers in live data.
Does grounding guarantee accuracy?
No. It sharply reduces errors, but the answer is only as accurate as the sources it's grounded in. Stale or conflicting documentation can still produce confident wrong answers.
関連用語
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique where an AI model retrieves relevant information from your own knowledge sources at query time and uses it to ground its answer, instead of relying only on what it memorized during training..
AI Hallucination
An AI hallucination is when a language model generates a response that is fluent and confident but factually wrong or fabricated — inventing details, policies, or sources that don't exist..
Knowledge Base
A knowledge base is a structured, searchable library of articles — how-tos, FAQs, troubleshooting guides, and policies — that lets customers or agents find answers without contacting support directly..
Tool Use
Tool use is the ability of an AI model to invoke external functions, APIs, or systems — like looking up an order or issuing a refund — instead of only generating text, so it can act on real data rather than just describe it..
AI Guardrails
AI guardrails are the rules, checks, and constraints placed around an AI system to keep its behavior safe, on-topic, and within policy — controlling what it can say, do, and access..
アイデアを実装に
Macha は既に運用中のヘルプデスク (Zendesk、Freshdesk、Front、Intercom、Gorgias) の上に載る AI エージェント層です。
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