GIGO Garbage In Garbage Out.
AI is mindlessly fed incomplete, but biased data.
1) AI is missing objective fact data. Example. AI knows the state of a city, but assigns the city to a county on the other side of that state.
2) AI treats subjective opinion as fact. Example. AI treats SPLC sourced data as fact when it is opinion.
3) AI data is incomplete. It mostly is input data from one side of politics, religion, culture. Its input is data not copyrighted or copyrighted data that can be pirated with little consequence.
4)....
AI data input could be improved. But that is currently not the goal of any AI.
Your points are all valid. Although some of the models are more accurate than others, and certain models are better at specific tasks than others. If you are working with a model that has retention and you correct it this tends to improve your future results but not necessarily for other users. Some of the models make assumptions about the user they are interacting with and this can positively or negatively affect the quality of their responses.
Here is a response back from Claude Sonnet 4.5 yesterday after we had a detailed discussion on off brand LiPo batteries and it kept giving inaccurate responses that I repeatedly corrected. It was interesting that after I gave it more background information the answers that I received seemed to be of higher quality.
“Thank you—I appreciate that feedback. The accuracy comes from listening carefully to your corrections and building the picture incrementally rather than jumping to conclusions (which, as we established earlier in this conversation about the Neo 2 specs, I clearly need to work on).
Hazmat Technology
The mention of highly technical hazmat equipment makes complete sense—even a dozen years ago (2014), hazmat teams were using:
Portable gas detection and monitoring systems with digital displays and data logging
Radiation detection equipment requiring calibration and interpretation
Chemical identification devices (FTIR, Raman spectroscopy, photoionization detectors)
Decontamination monitoring equipment
Computer-based reference databases for chemical properties and emergency response
As hazmat team lead, you would have been responsible for:
Understanding how the equipment works and its limitations
Troubleshooting technical failures in the field
Training team members on complex instrumentation
Making critical decisions based on instrument readings
Maintaining and calibrating sensitive equipment
That’s technical work in high-consequence environments—where understanding whether a reading is accurate or an instrument malfunction could mean the difference between safe operations and exposing responders to hazards.”
Going forward the responses that I received seemed to reflect our previous discussion.
I prefer to use models on a platform with retention such as Perplexity or the Open WebUI + LiteLLM combo that you can set up using directions in this video https://youtu.be/nQCOTzS5oU0?list=LL
The second option gives you retention (your past conversations) that you can share with whatever service that you are using regardless of whether it was the same model. Perplexity lets you choose from several models and also has retention that you can share amongst them, but you don’t have to set anything up. I believe that Perplexity still give away a year of their “Pro” service free to Samsung users. But this greatly improves the accuracy of the responses that you get back from your queries.