Does anyone here still have an AI hallucination problem? I've ran 700+ tests. over 100 different models, I killed ai Hallucination's for every ai model, it's gone. Flawless results. Does anybody need help??

[ryanjordan11]

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**Does anyone here still have an AI hallucination problem?

I’m asking seriously.

I’ve now run 700+ tests across 100+ different models and I’m getting flawless results.

No hallucination.
No drift.
No memory decay.
No “why the hell did it do that?”**

Same architecture. Different models.

So I’m curious:

Are people still treating hallucination like an unavoidable property of AI?

Because I don’t think it is anymore.

If anyone is fighting this in production, I’d love to see what’s breaking.

I've solved this back in March. figured out how to do it in December last year and went to work building it.

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[ryanjordan11]

Would this help any of you? https://youtu.be/NHPW_ZbcRZ8?si=plYm7gUFoHYaXsNQ

[ryanjordan11]

The biggest shift for me was realizing the model was never the thing I needed to trust.

The system around it was.

So I stopped asking, “Which model hallucinates less?” and started asking, “How do I make hallucination irrelevant?”

That changed everything.

Now the model can change.
The context can’t.
The source of truth is controlled.
The agent can retrieve exactly what it needs.
And the behavior stays consistent across models.

That’s the part I think people are still underestimating.

[endbarggowpeev-arch]

The distinction between the model and the system around it is an interesting one. Treating the model as a component rather than the source of truth can definitely make hallucinations much easier to control.

I’d be interested in how you’re measuring “flawless results,” though. In production, I’d expect the evaluation to include adversarial or ambiguous inputs, missing information, retrieval failures, and cases where the correct answer simply isn’t available in the context.

The part about keeping the context and source of truth controlled seems especially important. If the agent can only retrieve from verified sources and there’s a clear way to refuse or flag unsupported answers, changing the underlying model becomes less risky.

I’d be curious to see the architecture and evaluation methodology, particularly how you distinguish genuinely eliminated hallucinations from cases where the system simply avoids answering.