7d • General Discussion 💬
When an LLM Sounds Confident and Is Wrong
Bad information costs time, credibility, and decision quality.
I recently asked an LLM to verify details of a historical process I already understand end to end.
The source material dates back to around 2015.
I was not asking what happened.
I already knew the outcome.
I was asking for structural specifics.
The model gave me outdated and incorrect information.
I challenged it multiple times.
Each time, it doubled down.
What mattered most was the explanation it gave at the end:
“You should never rely on an LLM as a primary or sole source of truth. I am a tool for processing language, not a knowledge retrieval system with guaranteed accuracy.”
That is not an apology.
It is a boundary.
LLMs generate answers that sound confident, even when the underlying data is incomplete or missing.
If you do not already know the domain well enough to challenge the output, you may never realize it is wrong.
Use AI for synthesis, drafting, and exploration.
Do not use it as a source of truth.
Verify.
Cross-reference.
Validate.
AI amplifies judgment.
It does not replace it.
⸻
TL;DR
LLMs can sound extremely confident while being completely wrong, especially on older or niche details.
Use AI for speed and synthesis, not as a source of truth.
If accuracy matters, verification is part of the workflow.
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When an LLM Sounds Confident and Is Wrong
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