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How do you keep your wiki from evolving into a kludge of random-ish info? One example of tacit knowledge is knowing the Dell T420s Tower Servers have very poor

How do you keep your wiki from evolving into a kludge of random-ish info? 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.

jsty 3y ago
Use an internal Q&A / Quora-style tool with good search. Trying to keep little snippets of info like that under useful MECE categories only ever ends in pain & frequent refactoring.
lucidguppy 3y ago
I don't want to get too much on the ai hype train - but it seems like this would be perfect for machine learning, trained on the wiki-snippets of the company and emails (initiator would have to set a flag saying this is going to be training data).
- sonicanatidae 3y ago
  That would be a good use of LLM AI, imo. Good idea.
- cratermoon 3y ago
  > machine learning 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.
  - sonicanatidae 3y ago
    Since this is the internet, I'm asking, not arguing...just this time.. ;) 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?
    - cratermoon 3y ago
      > 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. 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-artificial-intelligence-b2409356.html https://www.independent.co.uk/tech/ai-water-energy-artificia... 2. https://spectrum.ieee.org/ai-energy-consumption https://spectrum.ieee.org/ai-energy-consumption
      - sonicanatidae 3y ago
        Sure. I get that AI doesn't "know" things. 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!
- nicbou 3y ago
  How can someone do this, concretely? I would love to slowly train an AI to fit the gap between search and one on one assistance, but I don’t know how to do it as a single person running an informative website.
rout39574 3y ago
That's what a wiki is good for: collecting snippets of hard to categorize information. If you've got a culture of recording those notes, then you can develop full text search habits. 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.
- pixl97 3y ago
  Even this ends up very messy. 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.
williamdclt 3y ago
Put it in the runbook for the alarm you got when the server failed, or the maintainance guide that your team runs through every month. Or make heatsink repasting a regular documented process, or something similar. 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
- sonicanatidae 3y ago
  I'm a fan of the toyota approach, but sadly, Dell isn't. lol I'll review the runbooks. We have a number of them, mostly created by me. :)
j45 3y ago
Not OP, but you have to regularly review and adjust the ontology regularly as more articles are added. 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.
- sonicanatidae 3y ago
  Sure, but this then becomes a fulltime role by itself, once the info grows enough. 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. ;)
  - j45 3y ago
    Manually, yes. 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.
krisoft 3y ago
> 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. 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".
- sonicanatidae 3y ago
  This gets muddy real fast. 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
  - soks86 3y ago
    That absolutely sounds like it. 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.
  - krisoft 3y ago
    > One could argue that tacit knowledge also applies when it comes to the feel of pulling laptops apart without breaking the tiny little clips. I agree that that is tacit knowledge. > This gets muddy real fast. I don't see how your example demonstrates the muddyness?
- PH95VuimJjqBqy 3y ago
  it kind of blows me away how many people on these forums don't know what tacit knowledge is. 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.
nicbou 3y ago
I write documentation for a living and refactoring is a significant part of my work. Keeping documentation tidy is a full time job.

jsty 3y ago

Use an internal Q&A / Quora-style tool with good search. Trying to keep little snippets of info like that under useful MECE categories only ever ends in pain & frequent refactoring.

lucidguppy 3y ago

I don't want to get too much on the ai hype train - but it seems like this would be perfect for machine learning, trained on the wiki-snippets of the company and emails (initiator would have to set a flag saying this is going to be training data).

sonicanatidae 3y ago
That would be a good use of LLM AI, imo. Good idea.
cratermoon 3y ago
> machine learning 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.
- sonicanatidae 3y ago
  Since this is the internet, I'm asking, not arguing...just this time.. ;) 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?
  - cratermoon 3y ago
    > 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. 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-artificial-intelligence-b2409356.html https://www.independent.co.uk/tech/ai-water-energy-artificia... 2. https://spectrum.ieee.org/ai-energy-consumption https://spectrum.ieee.org/ai-energy-consumption
    - sonicanatidae 3y ago
      Sure. I get that AI doesn't "know" things. 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!
nicbou 3y ago
How can someone do this, concretely? I would love to slowly train an AI to fit the gap between search and one on one assistance, but I don’t know how to do it as a single person running an informative website.

rout39574 3y ago

That's what a wiki is good for: collecting snippets of hard to categorize information. If you've got a culture of recording those notes, then you can develop full text search habits. 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.

pixl97 3y ago
Even this ends up very messy. 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.

williamdclt 3y ago

Put it in the runbook for the alarm you got when the server failed, or the maintainance guide that your team runs through every month. Or make heatsink repasting a regular documented process, or something similar. 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

sonicanatidae 3y ago
I'm a fan of the toyota approach, but sadly, Dell isn't. lol I'll review the runbooks. We have a number of them, mostly created by me. :)

j45 3y ago

Not OP, but you have to regularly review and adjust the ontology regularly as more articles are added. 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.

sonicanatidae 3y ago
Sure, but this then becomes a fulltime role by itself, once the info grows enough. 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. ;)
- j45 3y ago
  Manually, yes. 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.

krisoft 3y ago

> 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. 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".

sonicanatidae 3y ago
This gets muddy real fast. 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
- soks86 3y ago
  That absolutely sounds like it. 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.
- krisoft 3y ago
  > One could argue that tacit knowledge also applies when it comes to the feel of pulling laptops apart without breaking the tiny little clips. I agree that that is tacit knowledge. > This gets muddy real fast. I don't see how your example demonstrates the muddyness?
PH95VuimJjqBqy 3y ago
it kind of blows me away how many people on these forums don't know what tacit knowledge is. 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.

nicbou 3y ago

I write documentation for a living and refactoring is a significant part of my work. Keeping documentation tidy is a full time job.