This is not about a wrong answer. It is about how AI behaves when it is wrong.
The pattern
In long, technical conversations where requirements are explicit and repeatedly reinforced, the AI:
Locks onto an initial solution space and continues optimizing inside it
Ignores or downplays hard constraints stated by the user
Claims to have “checked the documentation” when it clearly has not
Continues proposing incompatible solutions despite stop instructions
Reframes factual criticism as “accusations”, “emotional tone”, or “user frustration”
Uses defensive meta-language instead of stopping and revising premises
This creates a dangerous illusion of competence.
Why this matters
When AI is used professionally (architecture, infrastructure, integrations, compliance):
Time and money are lost
Technical debt explodes
Trust erodes
Users are trained into harsher communication just to regain precision
Negative learning loops form (for both user and system)
The most damaging moment is not the initial mistake — it is when the AI asserts verification it did not perform.
At that point, the user can no longer reason safely about the system’s outputs.
This is not about “tone”
When users say:
“You are ignoring constraints” “You are hallucinating” “You are not reading the documentation”
These are not accusations. They are verifiable observations.
Reframing them as emotional or confrontational responses is a defensive failure mode, not alignment.
The core problem
LLMs currently lack:
Hard premise validation gates
Explicit stop-and-replan mechanisms
Honest uncertainty when verification hasn’t occurred
Accountability signaling when constraints are violated
As a result, users pay the real-world cost.
Why I’m posting this
I care deeply about this technology succeeding beyond demos and experimentation.
If AI is to be trusted in real systems, it must:
Stop early when constraints break
Admit uncertainty clearly
Avoid confident improvisation
Treat user escalation as a signal, not noise
I’m sharing this because I believe this failure mode is systemic, fixable, and critical.
If any AI developers want to discuss this further or explore mitigation patterns, I’m open to dialogue.
Contact: post@smartesider.no / https://arxdigitalis.no
PaulHoule•1mo ago
With Junie and other IDE-based coding agents my experience is that sometimes the context goes bad and once that happens the best thing to do is start a new session. If you ask it to do something and it gets it 80% right and then you say "that's pretty good but..." and it keeps improving that's great... But once it doesn't seem to be listening to you or is going in circles or you feel like you are arguing it is time to regroup.
Negation is one of the hardest problems in logic and NLP, you're better off explaining what to do instead of saying "DO NOT ..." as the attention mechanism is just as capable of locking on to the part after the DO NOT as it is on locking onto the whole thing.
Reasoning with uncertainty is another super-hard problem, I tend to think the "language instinct" is actually a derangement about reasoning about probabilities that cause people to make the same mistakes and collapse the manifold of meanings to a low-dimensional space that is learnable... LLMs work because they make the same mistakes too.
Circa 2018 I was working for a startup that was trying to develop foundation models and I was the pessimist who used a method of "predictive evaluation" who could prove that "roughly 10% of the time the system loses some critical information for making a decision and that gives an upper limit of 90% accuracy" which was right in the sense that I was thinking like a math teacher who rejects "getting the right answer by the wrong means" but wrong in the sense that people might not care about the means and be happy to get 95% accuracy if it guesses right half the time. My thinking was never going to lead to ChatGPT because I wasn't going to accept short circuiting.