The science of stopping AI from “making stuff up” is taking a big leap forward — here’s what it means for you.
Have you ever asked an LLM a question… and it answered with something that sounded confident but turned out to be wrong? That’s what’s often called an AI hallucination: the machine didn’t just guess wrong; it invented facts.
A fascinating new research paper (soon to be published) by Leon Chlon, Ph.D. (follow him on LinkedIn here: https://www.linkedin.com/in/leochlon/) says these mistakes aren’t random at all; they happen for predictable reasons. Even better, we can already use these insights to spot them before they happen and (sometimes) prevent them entirely.
Let’s break it down.
AI Doesn’t Forget Randomly, It Compresses
Think of your AI as a student with a very small notebook. When you talk to it, it tries to summarize all the relevant facts into the tiniest, most efficient set of notes possible.
Most of the time, it does a great job; it’s like a student who can ace the test just from those notes. But sometimes, those notes leave out a tiny detail that turns out to be critical for one specific question you ask later.
When that happens, the AI overlooks the missing detail, it just “fills the gap” with something that seems statistically likely… and you get a hallucination.
The New Science: Predictable Hallucinations
The researchers call this a “compression failure.”
They discovered:
- AI models are almost “perfect reasoners” on average, but not always for each single answer.
- The chance of a hallucination depends directly on how precise the information the model has for your question is.
- The less info in context, the more it will improvise confidently.
Most importantly, you can measure or sense when the AI is about to do this.
How to Make AI Stop Hallucinating
Here’s the practical playbook that comes right out of this research, adapted for non-scientists like us:
1. Give it everything upfront
AI works best when all the clues are in front of it at once. Don’t drip-feed partial details. Include all relevant facts in your prompt to ensure it has the whole “notebook” before answering.
Goal: Reduce hallucinations by front-loading the model’s working memory with all relevant context.
Sample Prompt:
“Here’s the full project background, goals, and constraints in one message so you have the complete picture before answering:
[Paste or attach full project brief as a File in AlmmaGPT]
Please read and tell me you have all the details you need before giving your answer.”
AlmmaGPT feature to recommend:
- Files → Upload your full reference material so the AI has all the facts at hand.
- Reusable Prompt Presets → Store a “Full Context First” template for tasks where missing details cause big errors.
2. Ask it if it’s sure
Tell it: “If you’re not certain, say you don’t know”. This nudges the AI to refuse rather than hallucinate. The researchers found that letting the model say “I can’t answer with confidence” brought hallucinations down to zero, with only about 1 in 4 questions being refused.
Goal: Force the model to self-check confidence and refuse if uncertain.
Sample Prompt:
“Answer only if 90% confident. If you cannot be sure, reply: ‘Not enough info – please provide more details.’
Question: How did policy X affect company Y’s revenue in 2022?”
AlmmaGPT feature to recommend:
- Custom AI Agents → Build an “Honest Answer Agent” that automatically applies a high-confidence rule to all responses.
- Memories → Store your “refusal if unsure” instruction so it applies across all conversations.
3. Check for “confidence lag”
If an answer comes too quickly for a complex question, be wary — that may mean it’s guessing from its compressed notes instead of “reasoning through” with the details.
Goal: Spot when the AI answers too fast for a complex question (signal it may be guessing).
Sample Prompt:
“Before answering, take a few seconds to deliberately think and outline your reasoning steps. Don’t go directly to the final answer. First, list the facts you are using, then answer.”
AlmmaGPT feature to recommend:
- Agents → Create a “Deliberation Agent” that always responds with a structured reasoning breakdown before final output.
- Bookmarks → Save examples of “fast but wrong” cases to refine your prompts later.
4. Feed it extra specifics
Every extra precise fact reduces hallucination chances. The paper quantified it: each additional piece of solid information significantly lowers the likelihood.
Goal: Actively reduce hallucination risk with more concrete facts.
Sample Prompt:
“You previously said the report was published in June. Here’s the exact PDF of the report [attach file or paste extract].
Using this specific source, answer: What were the top three findings?”
AlmmaGPT feature to recommend:
- Files + Annotations → Attach primary sources and reference them directly in your prompt.
- Memory → Teach the AI always to request additional details if context feels incomplete.
5. Use AI as a co-pilot, not a truth oracle
Treat its output as a draft, not gospel, especially for subjects that can be verified elsewhere.
Goal: Treat AI output as a draft to be refined, not the final truth.
Sample Prompt:
“Draft an outline for a blog post on [topic]. Include a ‘Check & Verify’ column for each point so I can confirm facts before publishing.”
AlmmaGPT feature to recommend:
- Custom Agent → Make a “Co-Pilot Writer” that automatically outputs double-check checklists.
- Bookmarks → Save partial drafts and resume them later after human verification.
✅ With AlmmaGPT, these prompts could be saved as Presets, tied to Agents built for accuracy, and combined with Memory so that every time you work on high-stakes tasks, these safety steps apply without re-typing them.
What’s Coming Next
The paper’s most significant promise is predictive anti-hallucination tools built right into AI systems.
Here’s what’s likely in the near future:
- “Risk meters” in chat interfaces indicate whether the AI believes it’s likely to hallucinate.
- “Bits-to-Trust” counters telling you how much more info you need to provide for a confident answer.
- Refusal modes that politely say, “I don’t have enough info to answer that without guessing,” making AI a lot more trustworthy.
At Almma, we’re watching these developments closely. They fit perfectly with our mission: AI Profits for All — with AI you can trust.
Our AlmmaGPT platform already supports custom flows where you can:
- Feed full context documents into the AI without token limits getting in the way.
- Create custom AI agents that always sanity-check their answers.
- Design prompts that require the AI to flag uncertain claims.
Bottom Line
Hallucinations aren’t magic, mystery, or malice — they’re the result of too much compression and not enough detail.
The good news: you can make them rare by front-loading information, encouraging honesty, and knowing when to double-check.
AI won’t be perfect tomorrow — but starting now, you can make it far more reliable in your work, your research, and your business.
Pro Tip for AlmmaGPT users
When building your AI agent:
- Use a context primer at the start of every conversation (store it as a preset).
- Add a confidence check prompt at the end of each answer.
- Encourage the model to write: “Not enough information to answer accurately” rather than guessing.
That’s how you beat hallucinations — not with wishful thinking, but with better information hygiene.
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