One example of tacit knowledge is knowing the Dell T420s Tower Servers have very poor cooling for the raid controllers and often, this can cook them over time, requiring replacement or, at least repasting the heatsink.
Now imagine 100s of those little tidbits. Useful, but hard to categorize.
A kluge of randomish info by people who make occasional attempts to organize and garden... sucks compared to a beautifully manicured knowledge base. But it's fantastic compared to a blank wall and a shrug.
The first problem I commonly see is called "Delores Thesaurus". It turns out people find like 50 different ways to say the same damned thing. Maybe with LLM based sentiment search we don't have to be so exact in how people ask the same question?
Also this information getting stale or out of date commonly leads to people not going back and gardening because of the massive amount of work involved, such as adding versioning when you figure out there are 10 different product behaviors over time.
I don't even think that is tacit knowledge. Tacit knowledge is how do you know when the edge of your chisel is too dull and need re-honing. (It feels off.) Or knowing how much weight transfer you need to safely navigate a given turn with a motorcycle.
Or to grab a more similar example: Tacit knowledge is looking at a computer case you have never seen before and thinking "that doesn't look like enough cooling, we should calculate and check".
One could argue that tacit knowledge also applies when it comes to the feel of pulling laptops apart without breaking the tiny little clips. It takes a few before you get the feel for it, because the difference between snapping a tiny little case clip and not snapping it are about 1 psi (borrowing a term) difference..lol
I opened several laptops with spudger tools. Now I can open random items I didn't watch videos for just because I have an idea of how it _might_ be assembled.
I agree that that is tacit knowledge.
> This gets muddy real fast.
I don't see how your example demonstrates the muddyness?
tacit knowledge is being able to ride a bicycle.
You'll never describe it well enough that a child can hop on the bicycle and ride it the first time.
the knowledge that enables someone to successfully ride a bicycle is tacit knowledge.
I know how a team is organized, I knew the multiplayer feature was tacked on (probably by interns), I know about network code edge cases, bada-bing bada-boom I can trigger a bug and understand why it happens without ever seeing their code.
Or sitting in a theater and just noticing the lighting setup and transitions rather than the performance itself.
Basically, as Toyota says, build quality _into_ each part of the product rather than layering it on top by documenting discrete pieces of info that an operator has to discover, understand and act upon
I'll review the runbooks. We have a number of them, mostly created by me. :)
This is already kind of skewed by hype. It's not possible to teach a transformer architecture LLM the fact "Dell T420s Tower Servers have very poor cooling for the raid controllers". All that can be done is have a corpus of text where the statistically most likely text associated with a prompts about "raid controller cooling Dell T420" includes the text "Dell T420s Tower Servers have very poor cooling for the raid controllers", or some variant of it. But the model doesn't know that fact, or any other fact, so it's not being taught this knowledge.
In this context, can we assume AI would be a good internal wiki assistant/Mod?
It could make many data correlations, rapidly, and suggest or act upon migration/collation/etc of information into more useful groupings, with related information automatically indexed and grouped, based on the more commonly searched for terms that match data related, or tagged. This could/would evolve over time, assuming the same AI and a changing team/staff, terms, current in-use hardware, in this case.
A simplified version of what I'm trying to say might be, AI can't know a fact, but it could parse inputs and build relationships between bits of data, based on how the users access that data, and under what headings it's commonly searched for.
An AI, especially the parse/response loop of LLM AIs, seems almost custom built to find that input, and match it with similar/related inputs and store the information in a more effective way, under more commonly searched headings from the users searching the wiki.
Am I missing something?
Yes, but so can text analytics tools that are not transformer model LLMs, and they can do it without thousands of swimming pools of water[1] or a gigawatt hour of electricity[2]. Those LLMs are just algorithmic summary generators, and text summarizers have existed for some time.
> find that input, and match it with similar/related inputs and store the information in a more effective way
But that's not how these automation tools work – they don't store information, they generate statistically likely text based on the training corpus.
1. https://www.independent.co.uk/tech/ai-water-energy-artificia...
My specific suggestion was about leveraging the parsing and response loop of LLM AIs to analyze free text, parse what's useful and the reply would be collating the data.
Thanks for taking the time to reply!
In short, the categorization and organization is key, and you have to do it when the number of documents is not only increasing, but different enough.
Sometimes, I just write a longer document and once it's ready, it can be broken into smaller ones. Doing this helps save the organization tremendous amounts of time in not having to re-learn things, or dig it up.
I maintain it for my teams, but as things grow, it becomes less manageable, since I'm also a T3 and have technical tasks to address, as well management of the teams.
The good news in all of this, is in less than a decade, it'll all be someone else's headache. ;)
Using technology not so much.
I have had to live in similar scenarios as you and am solving this problem and have made some headway. Would be happy to listen and learn from your use cases on what did and didn't work for you.