Your AI just told you about a conference that never happened. Or quoted you saying something you’ve never said. Maybe it invented an entire case study, complete with compelling details and realistic outcomes.

Welcome to confabulation, the technical term for when AI fills in gaps with fiction that sounds absolutely convincing.

This isn’t a bug. It’s a feature. And once you understand why it happens, you can work with your AI’s nature instead of fighting against it.

The Pattern-Matching Brain

Think about how you finish someone’s sentence when they pause mid-thought. You’re not reading their mind, are you? No, you’re using patterns from every conversation you’ve ever had to predict what comes next. Sometimes you’re right. Sometimes you’re wildly off.

AI models work similarly, but at a scale that’s hard to imagine. They’ve absorbed patterns from billions of text examples, learning what typically follows what in human communication. When you ask about a marketing conference in 2019, the model knows conferences have names, dates, locations, and speakers. If you haven’t given it specific details, it helpfully supplies ones that fit the pattern.

The problem? The model can’t distinguish between completing a pattern with real information versus plausible fiction. Both feel equally “correct” to its pattern-matching system.

Why Grounding Changes Everything

Here’s where most people get frustrated. They want their AI to just “know” what’s real versus what’s made up. But that’s like expecting your autocomplete to only suggest words that are factually accurate in your specific context.

Grounding works because it gives the AI a smaller, controlled dataset to pattern-match against. Instead of pulling from its vast training data (which includes both facts and fiction), you’re saying: “Only use these specific pieces of information I’m giving you.”

This changes the whole dynamic. When I provide an AI with a transcript of a client meeting and ask it to summarize key decisions, I’m not asking it to guess what probably happened in meetings like this one. I’m asking it to work only with what actually was said.

The Confidence Trap

What makes confabulation particularly tricky is how confident AI sounds when it’s completely wrong. The model doesn’t have an internal uncertainty meter. It generates text with the same tone and authority whether it’s recounting verified facts or creating elaborate fiction.

This confidence can be incredibly misleading. I’ve seen people accept obviously fabricated information simply because the AI presented it with such certainty. The lesson? Never use AI confidence as a proxy for accuracy.

Working with the Tendency

Instead of viewing confabulation as something to eliminate, think of it as understanding your AI’s personality. Some people are natural storytellers who embellish details. Others stick strictly to what they know. Your AI is definitely in the first category.

When you need creativity and brainstorming, this tendency is actually helpful. Ask your AI to generate hypothetical case studies or explore possible scenarios, and its pattern-matching abilities create rich, detailed examples that feel realistic because they’re built from real patterns.

But when you need accuracy, you have to constrain the sandbox. Provide source material. Set clear boundaries. Ask the AI to indicate when it doesn’t have enough information rather than filling gaps.

Practical Boundaries

The most effective approach I’ve found is being explicit about what you want the AI to do when it encounters gaps. Instead of hoping it won’t make things up, tell it what to do instead.

Try prompts like: “Based only on the attached document, summarize the main points. If information isn’t included in the document, say ‘not specified in the source material.‘” This gives the AI permission to admit ignorance rather than improvising.

You can also ask for citations or references for any specific claims. When the AI has to point to where it found information, it’s much more likely to stick to what’s actually there.

The Verification Step

Building verification into your process makes confabulation manageable rather than problematic. After getting AI output, ask yourself: Can I trace this back to something real? Does this align with what I actually know?

For important work, consider a two-step approach. First, get the AI to process and organize information you’ve provided. Then, in a separate prompt, ask it to fact-check its own work against the original sources.

Some organizations are implementing systematic approaches to this, using one AI to generate content and another to verify it against source materials. It’s an extra step, but one that builds trust in the output.

Making Peace with AI Nature

The goal isn’t to make AI behave like a perfectly accurate search engine. That’s not what these models are designed to do. They’re designed to understand context, generate human-like text, and work with patterns in ways that feel natural and helpful.

When you align your expectations with how AI actually works (brilliant at pattern recognition, terrible at distinguishing fact from plausible fiction) you can design interactions that leverage its strengths while protecting against its weaknesses.

Your AI will always be a creative collaborator that needs clear direction. Give it good source material, set clear boundaries, and verify important claims. Do that, and confabulation becomes just another trait to manage, like working with any talented but imperfect team member.