Hallucination — When AI Gets It Wrong

Hallucination — When AI Gets It Wrong

To understand why AI sometimes produces things that aren’t true, it helps to understand — even briefly — what it’s actually doing when it generates text.

How AI Actually Works

As we touched on in Lesson D1-02, AI language models don’t work like traditional software. They don’t look up answers in a database or follow a set of programmed rules. At their core, they analyze the mathematical relationships between words — their patterns, their connections, their context — and predict what comes next.

Here’s what’s remarkable: given this approach, it’s amazing how much AI gets right. It can draft coherent proposals, summarize complex documents, analyze funder priorities, and generate budget narratives that make sense. The predictions are right far more often than they’re wrong. The technology is genuinely impressive.

But sometimes the predicted text isn’t aligned with reality. A statistic that sounds plausible but doesn’t exist. A detail about your organization that’s slightly embellished. A claim that feels right but isn’t quite. In the AI world, this is called “hallucination” — though it’s worth noting that the entire output is generated through prediction. It’s not that the AI “knows” some things and “makes up” others. It’s predicting all of it, and most of the time it gets it right.

A note on modern AI tools: Many AI tools can now use web search and other capabilities to actually look up real-time information. When an AI searches the web, checks a database, or reads a document you’ve uploaded, it’s working with real data — not just predicting. This significantly reduces hallucination risk. But the underlying model is still predictive, so verification still matters.

What This Looks Like in Grant Writing

Hallucination in grant work shows up in a few ways:

Fabricated data points. A statistic that cites a real-sounding source but doesn’t actually exist. “According to the Bureau of Labor Statistics, 34% of…” — except that specific number isn’t in their data. Grant professionals are generally trained to verify citations and statistics, so these tend to get caught.

Embellished details. This is the sneakier category. AI might state that someone “served as executive director of a regional nonprofit before stepping up to lead this organization” — and that biographical detail simply isn’t accurate. These don’t trigger the same verification instinct because they don’t look like statistics or citations. They just read like facts.

Subtle overstatements. AI may describe your program’s outcomes in slightly more impressive terms than the data supports, or characterize a funder’s priorities in a way that’s close but not quite right. These are easy to miss because they feel approximately true.

The common thread: any time AI makes a factual or declarative statement, there’s some probability that it’s not perfectly accurate. You don’t need to check every adjective, but claims, facts, and specific details deserve attention.

The One Principle That Changes Everything

Here’s the most useful thing to understand about hallucination risk:

The ratio of input to output matters enormously.

If you give AI a large amount of source material — your organization’s outcomes reports, the full text of an RFP, past proposals, financial data — and ask it to do something relatively focused with that material, the risk of hallucination is very low. The AI has real information to draw from, and it stays close to it.

If you give AI very little context and ask it to generate a lot of content, the risk increases dramatically. With less source material to anchor its predictions, the AI fills in gaps with plausible-sounding text that may not be grounded in reality.

This is intuitive once you see it. An AI that’s summarizing a 30-page RFP you uploaded has very little reason to hallucinate. An AI that’s drafting a needs statement about a community it knows nothing about has every reason to fill in details that aren’t real.

Why Purpose-Built Tools Matter

This principle is also why a purpose-built AI tool tends to be more reliable than a general-purpose chatbot. A general ChatGPT or Claude account might have hundreds or thousands of conversation threads in its memory — personal questions, work tasks, random curiosities — all mixed together. That mixed context can confuse and conflate information.

A tool designed for grant work, where the AI has focused organizational context — your mission, your programs, your funder history, your past proposals — dramatically reduces the hallucination surface area. The AI has the right source material to work from, and it’s not pulling from an ocean of unrelated conversations.

How to Work With This

Rather than approaching AI output with paranoia, approach it the way you’d approach work from a capable but fallible colleague:

• Trust is proportional to context. The more relevant source material the AI had to work with, the more reliable the output
• Check declarative statements. Facts, statistics, biographical details, specific claims — give these a closer look
• Verify what matters most. You don’t need to fact-check every sentence, but the claims that go into a proposal or a funder communication deserve attention
• Provide rich context. The single best thing you can do to reduce hallucination is give the AI more of your actual data to work from

Philip’s Take: People hear “hallucination” and imagine AI just making things up constantly. The reality is more nuanced. AI gets an extraordinary amount right. When it doesn’t, it’s usually because it didn’t have enough context. Give it ample source material and ask for focused output, and the risk drops to near zero. That’s not a scary technology. That’s a powerful tool you need to learn to use well.

Hallucination is about what comes out of AI. The next risk is about what goes in: your data, and what happens to it.