ENGAGE
New agent live — RecruitmentAvg. response time 1.2sVisitor peak — 34 active43 agents active right now17 conversations in progress9 new leads this hour
ANALYSE
Top question: 'What does it cost?'91% matched to knowledge baseConversation peak 2–4 pmSentiment +8% positive847 interactions analysed14 patterns identified
IMPROVE
Knowledge base updated — 6 new docsConversion rate +18% this monthFAQ updated from top questionsResponse time down 12% since last week+34% accuracy after latest training3 agents fine-tuned by team
REPORT
ROI dashboard updated4 conversions reported todayMonthly report ready for 12 clientsWeekly digest sent5 new insights surfaced23 teams notified
← GLOSSARY

AI Grounding

Techniques that anchor AI responses to verified, factual information sources, preventing hallucination and ensuring accuracy.

AI Grounding is the practice of anchoring an AI system's responses to verified, authoritative information sources. Rather than allowing the model to generate answers from its general training data — which risks inaccuracy — grounding ensures every response is traceable to specific, approved content.

How Grounding Works

Grounding typically implements a retrieve-then-generate pattern, working hand-in-hand with knowledge base training:

  • Query analysis — understanding what information the user needs
  • Source retrieval — searching verified knowledge bases, documents, and databases for relevant content
  • Evidence-based generation — constructing responses that draw directly from retrieved sources
  • Citation and attribution — linking claims to their source material for transparency

Why Grounding Is Essential

For business-critical AI applications, accuracy is not optional — grounding is the single most effective defense against AI hallucination:

  • Incorrect product information damages credibility and can create legal liability
  • Fabricated policy details lead to broken customer expectations
  • Inaccurate advice in regulated industries carries compliance risk
  • Even small factual errors erode the trust that AI interactions depend on

Grounding vs. Fine-Tuning

While fine-tuning adjusts the AI model's weights to favor certain outputs, grounding operates at inference time — providing factual context with each query, often tuned further through prompt engineering. This distinction matters because:

  • Grounded content can be updated instantly without retraining
  • Sources are explicitly identifiable and auditable
  • The system can acknowledge when it lacks information rather than guessing
  • Accuracy is verifiable against the source material

Implementation in AI Agents

For customer-facing AI agents, grounding means every product detail, pricing claim, and policy statement comes from a maintained knowledge base — not from the model's imagination. This is particularly critical for an AI video agent, where confident delivery must be matched by factual accuracy.

The Trust Foundation

Grounding is what makes the difference between an AI that sounds helpful and one that actually is helpful. It transforms AI from a probabilistic text generator into a reliable information system.

See it in action

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