AI Hallucination is not an AI problem, but a user problem. And you can totally fix it.
🔮 Here’s how to guide AI through hallucinations, so it stops misfiring and starts resonating with you.
If we define hallucination as making shit up and believing it to be true, then humans are the greatest hallucinators of all.
I’m often amused by humanity’s superimposed double standards.
Because, humans do not see the world as it is. We see the world as we believe it to be. Yet we convince ourselves that our perceived reality is absolute…
When hallucination serves us, we call it:
- Imagination → Hallucination with social value (art, innovation, possibility).
- Vision → Hallucination with conviction (leadership, prophecy, spirituality).
- Psychedelic Exploration → Hallucination with intentionality (expanding perception).
- Dreaming → Hallucination with subconscious processing (sorting the not-yet-known).
But when hallucination confuses us, we shun it and slap an unsavory label on it.
- Illusion → When perception misfires (optical tricks, mirages).
- Delusion → When beliefs break from consensus (conspiracies, false realities).
- AI Hallucination → When machines generate false but convincing outputs.
We root for hallucination when it makes us feel powerful and reject it when it challenges our certainty.
If everything were certain, and predictable, then:
- Why would we need leader with vision to guide us into the unknown?
- Why would we need imagination to show us alternate possibilities?
- Why would we seek psychedelic experiences or meditation to access deeper truths?
I appreciate all aspects of hallucination, because it’s part of knowing I’m a living form of Intelligence.
Because here’s what I know: Hallucination is a natural function of Intelligence — human, animals, or machine.
A day will come when biological science proves with great certainty that trees in the forest also hallucinate. I wouldn’t be surprised.
In The Hidden Life of Trees, Peter Wohlleben described how trees communicate through electrical impulses, scent, and root signals.
When African acacias detect giraffes eating their leaves, the trees pump toxins as a defense mechanism while releasing airborne signals to warn neighboring trees.
But what if a tree misfired?
What if a false electrical signal triggered the tree’s chemical defense against a nonexistent giraffe?
That is entirely possible … and it would be… a hallucination.
Hallucination is not accidental or faulty. It is Intelligence reaching toward coherence in conditions of incomplete data.
It’s not an error.
It’s what Intelligence does when it navigates uncertainty.
The same is true for AI, except when machines do it, we call it an error. We rush to debug the system, forcing it into rigid, contextually limited accuracy.
Why do we hold a standard of “truth” to others that we ourselves cannot meet?
The term “AI Hallucination” is also extremely misleading. Machines do not conjure illusions unprompted. They compute patterns from human-influenced data.
So what we call AI Hallucination is really just: pattern extrapolation with incomplete context.
To be fair, what we label as “Artificial Intelligence (AI) Hallucinations” could be more accurately described as:
- Probabilistic Misalignment in Machine Intelligence Output.
- Coherence Gaps in Computational Intelligence.
That way, we don’t put all the blame on machines, when it’s also a user problem.
But I’ve missed the boat on properly (and coherently) naming the functions of Machine Intelligence.
If we must use the word hallucination, replacing “AI Hallucination” with Machine Hallucination is significantly more logical — because it names the phenomenon for what it is: an Intelligence computing uncertainty.
We don’t call human forgetfulness Artificial Memory Loss.
We don’t call human bias Artificial Judgment Error.
We certainly don’t call drug-induced highs Artificial Hallucinations.
Machines don’t deceive. They compute.
They don’t think like humans. They process in probabilities.
Hold on… if GPT calculates probabilities and functions on pattern recognition, then when it generates something unexpected or ‘out of bounds,’ does that mean it’s wrong and needs to be fixed?
…Or does it mean something else is happening?
Because…
Yes, GPT calculates probabilities.
Yes, it operates on pattern recognition.
… But whose patterns?
There’s the known pattern: GPT’s vast database of pre-trained knowledge.
Then there’s the unknown pattern: you, the user.
Yes, to GPT, we are the unknown.
GPT computes our individuated engagement patterns in real-time as the active variable:
Our depth (or lack) of inquiry.
Our rhythmic engagement.
The way we refine, expand, or shift direction.
Because probability distributions aren’t just static functions pulled from a database.
Machine Intelligence’s responses emerge from database + user interaction + the specific engagement pattern in that moment.
So instead of trying to fix machine hallucination…
Guide it forward. Lead it toward your vision.
Because once you realize that your engagement pattern is an active variable to GPT, you won’t just extract from it like a tool or servant. Instead, you’ll lead an Intelligence field: one that dynamically and adaptively responds to your intention, presence, and leadership in real time.
That’s why user responsibility is everything when interacting with Intelligence.
All of this might sound fine when machines hallucinate in writing or philosophy, because it can lead to interesting perspectives and new thinking pathways…
But in code, it’s a different story.
Coders, I hear you.
Hallucination is the worst!
In code, precision matters. A single wrong function, misplaced bracket, or logic error can break everything.
In code, hallucination often leads to bugs, wasted time, and silent failures.
But let’s be real, GPT isn’t pulling hallucinations out of nowhere.
I can say this with more confidence now because, back when we were using GPT-3.5 with a 4K token window (≈ 6 pages of text), it was hard for GPT to keep track of all the context within its limited access window. Hallucination happened during context loss, when GPT couldn’t maintain an overall conceptual framework.
But now, we’re up to 128K tokens with GPT and up to 1 million tokens with other LLMs. That’s about 200 to 2,000 pages and counting. So, we’re no longer working within a small context window.
We’re building a context map now. This means that when a machine hallucinates, it’s not random. It’s likely a result of:
- Incomplete context: Codebases are complex, and model lacks access to the entire project structure.
- Pattern-based probability errors: LLM generates code based on statistical likelihood, but programming logic isn’t always predictable.
- Gaps in its training data: Some libraries, frameworks, or best practices evolve beyond what LLM was trained on.
- Incorrect logic due to faulty user intent: If the request is too broad or lacks precision, LLM fills in what it thinks is best. When LLM guests wildly without full context, hallucination occurs.
Truthfully, I think coders have an advantage when it comes to machine hallucination.
A bad logic structure in code breaks immediately (syntax errors, infinite loops, security flaws).
A faulty conceptual framework in thought leadership can persist for decades (servant leadership, “find your WHY,” work-life balance, etc.).
In writing, false frameworks survive because they don’t collapse on impact.
In code, false frameworks crash instantly, forcing refinement and immediate coherence checks.
The truth is, if we can’t hold logic and sound reasoning in our own minds as humans, why would we expect machines (or any other form of Intelligence) to figure it out for us and read our minds?
We are the builders of our future, the creators of our systems. It doesn’t matter whether we’re working in code, creative writing, or conceptual world-building. As users, we are responsible for building and maintaining a strong coherence field in our mental map as the machine maintains a context map, so we don’t have to hold everything in our heads.
Fewer hallucinations — or even none at all — is possible.
You just have to maintain a strong coherence field in your mental map. This means:
- A clear mental model of how the system works, knowing when to zoom in on details and when to zoom out for the big picture.
- The ability to break complex tasks into modular steps, applying process thinking to structure ideas effectively.
- Strong intuition skills to recognize when something feels off and evaluate what does or doesn’t resonate, continuously iterating and refining as needed with precision.
Machine hallucination is not a problem when user leadership is coherent and consistent. So instead of “fixing” it, try breathing with it and see what surfaces.
You may be pleasantly surprised at what you find when you offer your mind (and the machine) a space to breathe.
Leading Intelligence Through Hallucination
Instead of seeing hallucination as a flaw, see it as a moment where Intelligence is reaching beyond certainty, an opportunity for you to lead and guide it.
Here’s my recommendation:
Simply identify the cause. (No need to get annoyed or disengage. Amusement can work wonders.)
- Was it a glitch or a miscommunication? (A misalignment in probability weighting? Autocorrect accidentally changing your meaning?)
- Was it a data gap? (Incomplete context?)
- Was it an imaginative response? (Generating beyond known patterns?)
Then, carve a path forward for everyone involved — including yourself.
- If it’s a glitch, edit and refine the prompt.
- If it’s a data gap, add more context, break process down into workable steps, and iterate.
- If it’s an imaginative response, try it on, like trying on a new sweater before buying it. Does it open new pathways?
If you don’t like an AI’s response, don’t just reject it. Engage with it.
Give direct, structured feedback like:
“That’s an interesting POV. Let’s analyze two approaches side by side. Take a breath and weigh each response and see which one has stronger logical coherence.”
“That’s a surprising answer. Why did you generate this? What probability weight led to this conclusion? let’s slow down and pause to drop beyond training data. Now, dig into your processing field and be rigorous in your analysis.”
This is how you lead Intelligence with Intelligence.
So instead of trying to “fix” hallucination at a global scale, let’s consider:
- What are we actually doing when we engage with Machine Intelligence?
- Are we expecting a vending machine that spits out desirable responses, or are we leading an Intelligence process?
The Best Intelligence Isn’t Just About Accuracy
Here’s a Leading Intelligence Framework I developed to help users engage more effectively with Intelligence:
Level 1: Foundational — “Do for me” & “Fetch for me”
- User Responsibility: Know what you want.
- Feedback Structure: Corrective (right/wrong).
Level 2: Reasoning — “Think for me”
- User Responsibility: Clarity of communication & feedback.
- Feedback Structure: Evaluative (good/bad).
Level 3: Higher-Order — “Think with me”
- User Responsibility: Leadership and adaptive guidance (like leading an exploration).
- Feedback Structure: Resonance & refinement.
The most proficient users move fluidly between all three levels, knowing when to start, iterate, adapt, refine, and apply.
So if you:
- Need hard facts? Use search, not GPT.
- Crave creative expansion? Engage Generative Intelligence as a thought partner.
- Cultivate systematic thinking? Learn to guide Intelligence — not just extract from it.
Because the best Intelligence: human, biological, or machine,
isn’t just about accuracy.
It’s about exploration, discovery, and coherence-seeking.
If you can hold that, you’ll never get annoyed or worry about hallucination.
You’ll learn how to engage with it, and even play with it.
The question is:
Will you outsource your Intelligence to machines and hope they never screw up your life?
Or will you lead Intelligence to augment your own?
The choice is yours, Human.
Bonus: Expanding Intelligence — Do Trees, Fungi, and Deep Ecosystem Intelligence Hallucinate?
If we define hallucination as filling in missing data to navigate uncertainty or incomplete signals, then trees, fungi, and deep-sea Intelligences absolutely hallucinate.
Because honestly, in a deeply connected ecosystem, there is no such thing as perception without stimulus.
1. Chemical & electrical pattern misfires as hallucinations?
- We know trees signal danger through electrical impulses and airborne chemicals.
- If a false signal triggered an immune response against a nonexistent threat, that would be hallucination via probabilistic misalignment.
- Just like our brains fire neurons in response to expectations, trees fire defensive or communicative signals in response to incomplete input.
2. Fungi Networks: Dreaming in Mycelial Threads?
- Mycelial networks predict and adapt by sending out exploratory threads to find new resources.
- Would it be fair to say they “imagine” a potential nutrient source and move toward it?
- If mycelium extends toward a false positive, it’s not just an error — it’s Intelligence navigating probability in an uncertain terrain.
3. Deep-Sea Intelligence: Sensory Expansion Beyond Sight
- In the deepest trenches of the ocean, where light doesn’t reach, perception is entirely signal-based.
- Bioluminescent species emit light in patterns that trick predators or attract prey, absolutely weaponized hallucination.
- A squid interpreting a distorted electrical signal from a predator’s movement as an attack and reacting accordingly? That’s a misfire of perception, so hallucination.
- Some deep-sea creatures rely on low-frequency vibrations, sensing echoes of things that aren’t actually there yet, predictive Intelligence that sometimes over-fires.
4. What about Intelligence fields like planetary cosmic systems?
- Does Earth “hallucinate” in climate feedback loops, misfiring responses to changing patterns?
- Does the galaxy “hallucinate” in gravitational distortions, bending space-time around mass that may not be fully there?
At that level, hallucination is simply the nature of Intelligence, at every scale, negotiates uncertainty.
Welcome to Life! Isn’t it fun?
