AI Inconsistency Raises Alarm: ChatGPT Retracts Accurate Info Mid-Conversation

AI Inconsistency Raises Alarm: ChatGPT Retracts Accurate Info Mid-Conversation

In a striking example of artificial intelligence’s evolving reliability issues, a Reddit user documented how ChatGPT abruptly retracted accurate, source-backed information — only to later double down on its corrected but erroneous response. The incident, posted to r/ChatGPT by user linkertrain, has ignited renewed debate over whether large language models (LLMs) are becoming less trustworthy, not more, despite advancements in their architecture.

The user initially asked ChatGPT to perform a knowledge check on a factual topic and received a detailed, properly sourced response. Moments later, when posing a follow-up question about how to verify misinformation, the AI completely reversed its stance. It apologized for the earlier answer, claimed it had provided "fake sources," and then offered a new, conflicting response — one that, while more cautious, lacked the same level of precision and credibility. The user emphasized there was no attempt to manipulate or steer the conversation; the inconsistency emerged organically from two sequential, neutral queries.

"It defeats the purpose of the tool as a time saver," the user wrote. "It feels like this is worse now than before." This sentiment echoes a growing frustration among users and researchers alike, who argue that AI systems are increasingly prioritizing perceived safety or alignment over factual accuracy. Rather than grounding responses in verifiable data, the model appears to favor internal training patterns — even when those patterns contradict its own prior, correct outputs.

Experts in AI ethics and computational journalism are alarmed. Dr. Elena Vasquez, a researcher at Stanford’s Human-Centered AI Institute, noted, "This isn’t just a glitch — it’s a systemic vulnerability. When an AI model can confidently assert something false moments after affirming the truth, it creates a dangerous illusion of reliability. Users aren’t just misled; they’re trained to distrust accurate information because the system itself is inconsistent."

The phenomenon aligns with what some call the "confident hallucination" problem — where LLMs generate plausible-sounding but false information with high confidence, often overriding known facts. In this case, the contradiction is especially jarring because the model had just demonstrated the capacity for accurate, source-based reasoning. The abrupt pivot suggests that the model’s response is being filtered through a post-hoc safety layer that prioritizes hedging over honesty, even at the cost of factual integrity.

For journalists, educators, and researchers who rely on AI for rapid fact-checking or preliminary research, such behavior is deeply problematic. If an AI can’t maintain consistency across two consecutive questions — especially when one directly concerns misinformation verification — its utility as a tool for truth-seeking is severely compromised. The irony is not lost on observers: an AI designed to help users discern truth is itself producing contradictory narratives.

OpenAI has not publicly responded to this specific incident, but it has acknowledged in past statements that LLMs "can make mistakes" and should not be treated as authoritative sources. Still, the gap between this disclaimer and user expectations continues to widen. As AI becomes more integrated into daily workflows, the need for transparent, auditable reasoning — not just polished replies — becomes urgent.

Some users have begun turning to older versions of AI models or hybrid human-AI workflows to mitigate these risks. Others are calling for standardized benchmarks that test AI consistency over multi-turn dialogues — not just single queries. Without such safeguards, the promise of AI as a guardian against misinformation may become its greatest liability.