What “Grounding” Means in AI—and Why It Matters

TLDR: Grounding refers to how an AI model connects abstract words to real world meaning. If your team is using GenAI for anything public-facing:policy, client content, CX: it matters whether the system understands what it’s saying. This post explains grounding in plain terms, how to spot ungrounded outputs, and what to do about it in your workflow.

What does “grounding” mean in AI?

In short: it’s about meaning with reference. A model is grounded when its output connects to something real, like data, context, sensory input, or traceable source material.  Without grounding, GenAI just mimics patterns. It generates fluent nonsense when confident but wrong. A recent Google DeepMind paper outlines this gap and the implications for reasoning, truth, and factual accuracy in AI systems.

• For real-world teams, this is a big problem. It shows up when:
• A chatbot gives confident but incorrect advice
• An assistant writes about regulations but can’t link to the actual source
• A report sounds impressive but no one can say where the numbers came from

Why grounding matters in your organisation

If you’re using GenAI to:

1. Draft reports
2. Write comms or policy documents
3. Generate client-facing answers
4. Build research summaries or internal briefings

You’re relying on the model to not just be fluent, but right. And most large models are trained on the open internet not your documents, data, or lived context. That’s where grounding breaks down.

We’ve seen this in the real world:

• A local council briefing note included fabricated citations because the model filled the gaps
• A grant application draft reused numbers from unrelated industries, lifted from a generic knowledge base
• A marketing team published outputs with subtle tone mismatches and false claims because prompts weren’t specific enough

How to spot ungrounded AI outputs

Watch for these signals in your team's work:

• Confident but unsourced claims: “According to recent studies…” with no link or paper
• Inconsistent data references: Different numbers for the same metric across outputs
• Overuse of vague phrases: “Leading experts agree,” “It is widely known,” etc.
• Smooth tone with shaky facts: It reads well but doesn't survive scrutiny

Ungrounded content feels persuasive but it crumbles under review. That’s why we train teams in our AI Fundamentals Masterclass to spot and stress-test these outputs early.

Grounding can’t be fixed after the fact. It needs to be designed into your GenAI usage.

Here’s how:

1. Use source material wherever possible
Prompt with real documents, datasets, and examples.
Instead of:

“Summarise council policy on renewable procurement”
Use: “Summarise the attached 2023 City of XYZ renewable procurement policy. Focus on targets and timeline.”

2. Require source flags in output
Train staff to ask for citations or links.
Prompt example:

“Include references to the original policy sections in parentheses.”
Or: “List the original file name and page number for each fact.”

3. Build review steps into process
Don’t allow AI outputs to go straight to client or publication.
Add review checklists like:

• Are claims traceable?
• Are sources real and up-to-date?
• Is tone aligned to our brand?
• Did we copy/paste hallucination without checking?

We install this into your AI Strategy Roadmap so teams don’t get burned later.

4. Test and retrain prompts
If a model keeps giving weak or vague results, the prompt is likely under-specified.
Use structured test cases: give it a real doc, a specific request, and compare outputs with manual work. Refine prompts over time and log what works. We do this live in the AI Bootcamp using your own materials.

FAQs

Q: What happens when a model isn’t grounded?
A: It produces fluent text that may be factually wrong, misleading, or completely made up. This becomes risky when used in public, legal, or policy settings.

Q: Can we force ChatGPT to cite sources?
A: Not reliably. You can prompt for it, but unless you upload documents or use a retrieval system (RAG), it often guesses. That’s why source-grounded prompting matters.

Q: How do I get my team to check for grounding?
A: Use a review checklist. Train for it explicitly. We build this into our Masterclass and Strategy Roadmap programs.

Q: Are hallucinations the same as ungrounded outputs?
A: Related, but not always the same. Ungrounded outputs lack reference; hallucinations invent content. Both require human review and process controls to manage.

Where to Start

• AI Fundamentals Masterclass: Teach your team how to prompt for grounded, referenceable outputs
• AI Bootcamp: Test prompt performance on your own policies, briefs, and client tasks
• AI Strategy Roadmap: Build quality assurance and review steps into your AI workflows
• Readiness Assessment: Find where your team may be relying on ungrounded content already—and how to fix it

Your AI doesn’t need to be perfect. But it does need to be credible. Grounding is how you make that happen.