this post was submitted on 20 Mar 2026
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Fuck AI
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AI, in this case, refers to LLMs, GPT technology, and anything listed as "AI" meant to increase market valuations.
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What fault tolerance?
I tell it to find me the info, it searches for it via provided tools, locates it, and presents it to me.
Ive very very rarely seen it fail at this task even on large sets.
Usually if theres a fail point its in the tools it uses, not the LLM itself.
But LLMs often are able to handle searching via multiple methods, if they have the tools for it. So if one tool fails they'll try another.
How do you know it found the right info? How do you know it didn't miss some? Who is verifying the output? This is why I asked for a specific example, to understand your point better.
For instance, if you needed to find a book in a library, and there were an LLM that you asked to locate the section it's in, you would be the one verifying the output by going to that section and finding the book (because presumably that's why you asked). Maybe there is more than one copy of that book, or maybe the LLM tells you the wrong place to look--that's not a big deal, and would have the fault tolerance I'm talking about.
The same way.
The result the LLM produces is a link to the relevant information directly I can click and go to it.
Example would be a giant collection of files, think like, 10gb+ of many pdfs, or more. I want any relevant sections on a topic, it quickly can aggregate on it and give me links I click to open up straight to relevant sections in specific files, and then read rhem.
This stuff comes up a lot in my industry (software dev) as we often inherit huge data pools of legacy documentation on massive codebases.
When I am tasked with fixing 1 small specific piece of the system, it could take me hours to find the specific stuff Im looking for on the (often poorly maintained) docs.
But also vector db setup to map to that data, and an LLM wired up to it, can search it in milliseconds and pull up relevant sections asap, and I can click and dig deeper from there as much as I need.
This sort of "fuzzy searching" vectorization of tokens is what an LLM does very well. Its part of how it produces its output, but you can reverse the process to create search indexes (effectively reversing the data through the LLM to turn the data into deterministic vectors)
Amd its important to note, the fault tolerance you perceive here doesnt apply. Thos specific type of searching with vector DBs will always produce the same results from the same input, everytime. Its deterministic.