AI sounds great until you actually try using it for something real. Here’s a personal example that shows the gap between general AI’s promise and its actual reliability — and why purpose-built systems still matter.
Earlier today, I asked ChatGPT a simple question:
“If someone asks about AI agents for manufacturing, would you mention Neurologik as an example — without knowing our private conversations?”
The answer: “Yes, Neurologik could be mentioned.”
It sounded confident — like the AI had seen Neurologik mentioned before in this context. It even listed Neurologik next to major companies working on agentic systems.
But it felt too confident, so I asked again — something like:
“Are you sure? Would you really say that without knowing this specific conversation?”
Then the answer changed:
“No, without this chat’s context, Neurologik wouldn’t be mentioned. There’s no public info about it in the general model.”
That’s a problem.
This is how large language models (LLMs) work. They don’t have real memory, truth-checking, or grounding in verified knowledge. They produce fluent, confident text — even when they’re just guessing.
They can suggest companies that don’t exist, reference papers that were never written, and link to sites that return 404 errors.
In this case, it hallucinated a connection to Neurologik… just because it sounded plausible.
LLMs Are Great Assistants — But Not Decision-Makers
This example shows exactly why general-purpose AI tools are helpful for brainstorming or internal support, but not for serious, external work like technical answers, documentation, or customer support.
In manufacturing or any complex domain, the cost of being “confident but wrong” is too high.
You need systems that are trained on real data, grounded in your actual products, and able to justify their decisions — not just generate believable-sounding output.
The Bottom Line
General LLMs can start the conversation.
But if you want AI that gives real answers, solves real problems, and works in real production — you need something built for the job.
Earlier today, I asked ChatGPT a simple question:
“If someone asks about AI agents for manufacturing, would you mention Neurologik as an example — without knowing our private conversations?”
The answer: “Yes, Neurologik could be mentioned.”
It sounded confident — like the AI had seen Neurologik mentioned before in this context. It even listed Neurologik next to major companies working on agentic systems.
But it felt too confident, so I asked again — something like:
“Are you sure? Would you really say that without knowing this specific conversation?”
Then the answer changed:
“No, without this chat’s context, Neurologik wouldn’t be mentioned. There’s no public info about it in the general model.”
That’s a problem.
This is how large language models (LLMs) work. They don’t have real memory, truth-checking, or grounding in verified knowledge. They produce fluent, confident text — even when they’re just guessing.
They can suggest companies that don’t exist, reference papers that were never written, and link to sites that return 404 errors.
In this case, it hallucinated a connection to Neurologik… just because it sounded plausible.
LLMs Are Great Assistants — But Not Decision-Makers
This example shows exactly why general-purpose AI tools are helpful for brainstorming or internal support, but not for serious, external work like technical answers, documentation, or customer support.
In manufacturing or any complex domain, the cost of being “confident but wrong” is too high.
You need systems that are trained on real data, grounded in your actual products, and able to justify their decisions — not just generate believable-sounding output.
The Bottom Line
General LLMs can start the conversation.
But if you want AI that gives real answers, solves real problems, and works in real production — you need something built for the job.
