AI chatbots answer wrong questions just as confidently as right ones, and nothing in the response tells you which is which. The difference comes down to one thing: whether the AI looked it up or answered from memory. Here's how grounding works across ChatGPT, Claude, and Copilot, where it still fails, and what to demand before trusting AI with a financial figure
By
Alexandra
·
You ask an AI chatbot for a company's latest quarterly revenue and it answers instantly, confidently, with a specific number. The problem is that the number is wrong, and nothing in the response hinted that it might be. This happens because of one crucial difference in how AI tools work: whether they look things up before answering, or just answer from memory. Understanding this one distinction can save you from making a costly decision based on a well-worded guess.
The one question that matters: did it look it up?
Every AI answer falls into one of two buckets.
"Ungrounded" answers come purely from what the model absorbed during training, with no lookup, no source, and no way to check. Ask it about a company's latest earnings and it's essentially reconstructing an answer from patterns it saw months or years ago. For a number that changes every quarter, that's a guess dressed up as a fact.
"Grounded" answers work differently. The AI first retrieves real, current information (a filing, a webpage, a document you gave it) and then answers from that material, attaching a citation to back up each claim. If you can't click through to a source, you're not looking at a grounded answer.
That's really the whole concept. Everything else is just how each major AI tool implements it.
How the big three handle it
ChatGPT grounds its answers by running a web search and linking to the pages it used. Turn that off, or ask something outside its search scope, and it falls back to training data: fine for facts that don't change, risky for anything current.
Claude does this two ways: a web search tool for anything happening in the world right now, plus a separate "Citations" feature for when you hand it your own documents (a filing, a call transcript, a spreadsheet) and want every claim traceable to the exact sentence it came from. One financial firm using Claude for an automated research agent reported cutting made-up-source errors from around 10% down to nearly zero after switching this on. That's the company's own account, not an independent audit, but it's a useful data point.
Copilot leans on Bing search plus Microsoft Graph, the layer that holds your actual emails, files, and documents. In a business setting, that means Copilot can ground an answer in your company's own records rather than the open internet.
So how bad is it without grounding?
Here's the uncomfortable part: an ungrounded AI doesn't hedge. It doesn't say "I'm not sure." It answers just as fluently and confidently whether it's right or wrong, which means a wrong answer looks identical to a right one.
One 2025 academic study compared a model with web browsing turned on against the same model without it, on a task requiring lookups. The browsing version scored roughly four times better on accuracy. That gap isn't a finance-specific number, but it shows the scale of what real retrieval can add the moment a question depends on something the model never learned during training.
Where it breaks, specifically
The failures aren't random; they show up in predictable spots:
Anything recent. A number from this quarter, a metric published last week: the model never saw it, so it invents something plausible instead of admitting it doesn't know.
Combinations it never saw. Ask it to connect two facts it never encountered together, and it may quietly stitch together a combination that sounds right but isn't.
No warning label comes attached to either failure. That's exactly why grounding matters most for the kind of lookup where being wrong is expensive (a financial figure, a compliance detail) and matters less for something like brainstorming or drafting.
Grounding isn't a magic fix
It would be nice to say "just turn on grounding and you're safe." It's not that simple. Grounding solves the invented-from-nothing problem, but retrieval can still go wrong: pulling the wrong document, missing the right one, or getting confused by two sources that disagree.
A recent review of AI legal-research tools, tools specifically built around retrieval, still found hallucination rates as high as 33% in independent testing, far above what the vendors claimed. So grounding is the single biggest lever for cutting down errors, but "grounded" and "correct" aren't the same word. What the AI is grounded in, how current that source is, and whether it's actually retrieving the right thing for the right company and time period all still matter.
What "good grounding" looks like for a finance question
Not all grounding is equal. A model that grounds itself in a random blog post or forum comment is technically "grounded" but still shaky. For financial questions specifically, look for grounding that is:
Current: this quarter's numbers, not a stale cache
Entity-resolved: the actual company and ticker you meant, not a similarly named one
Point-in-time: reflecting what was actually knowable on the date in question
Traceable: citable down to the specific filing or document, not a vague "according to public sources"
That's the bar worth holding any AI tool to before you trust it with a number that affects a real decision.
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