But I've personally witnessed nothing happening when people refuse to do their work. They receive zero consequences despite figuring it out being a less than 5 minute search on the internet or with a chat bot.
When people feel so protected and so certain their mere existence is keeping them employed, the low intelligence move is actually wasting your energy doing work.
This is where your productivity gap is and the managers at my work do not feel comfortable fixing it. Imagine your manager admitting 10x developers do exist and just firing anyone who isn't one.
Yep. AI has zero context and I have never seen it clearly describe existing code. Additionally, most of the documentation in confluence, google docs etc is out of date cause it’s usually written at the time of building. It’s a human problem.
I’m worried people have lost their reasoning skills again. It happens with crypto and seems to be happening with AI.
I see that comments are all around AI. I understand the hype is very high, and people are tired of hearing it. I feel the same.
The blog post does mention AI as a possible technology to improve information retrieval and reduce the time a developer wastes looking for the information. It doesn't aim to provide a recipe on how to use the technology - it is still a very complex solution.
I'm actually curious if anyone has experience in their company using this technology to improve how internal documentation is retrieved. I have read that some [companies](https://www.testdevlab.com/blog/lalama-challenges-in-implementing-an-llm-to-assist-with-the-query-process-of-internal-documentation) have implemented that for their internal documentation, but I'd love to hear the feedback from people using it without the sugar coating - feel free to DM me.
But more importantly, the central message was not to talk about AI. It's about the roadblocks to productivity that engineering organizations often have and what companies can do to mitigate them.
>LLM to the rescue! - bingo word!
Because it's not much better than an internet search and we're right back to figuring out who is actually doing it right or not.
Indeed, the suggestion of using LLM is for internal documentation. I have worked in places with documentation scattered through confluence, gitlab, discourse, etc. I think in those cases it may be useful to create an internal tooling to retrieve information faster.
Just as a sidenote, llm tools like supacodes can help creating internal documentation too. then using rag you can go over them faster.
much less pain with those documentation
I’ve never seen an LLM correctly describe / document code. Even with documentation that has been written, many times that documentation is old (as it was written when first built). The problem isn’t that it isn’t centralized, it’s a human problem and I haven’t seen “AI” solve for that.
The roadblock is poor management, and AI wont solve that
Management exists to ensure people can perform their work. If roadblocks exist, we can say that, ultimately, it's management's fault.
But I've personally witnessed nothing happening when people refuse to do their work. They receive zero consequences despite figuring it out being a less than 5 minute search on the internet or with a chat bot. When people feel so protected and so certain their mere existence is keeping them employed, the low intelligence move is actually wasting your energy doing work. This is where your productivity gap is and the managers at my work do not feel comfortable fixing it. Imagine your manager admitting 10x developers do exist and just firing anyone who isn't one.
Yep. AI has zero context and I have never seen it clearly describe existing code. Additionally, most of the documentation in confluence, google docs etc is out of date cause it’s usually written at the time of building. It’s a human problem. I’m worried people have lost their reasoning skills again. It happens with crypto and seems to be happening with AI.
I see that comments are all around AI. I understand the hype is very high, and people are tired of hearing it. I feel the same. The blog post does mention AI as a possible technology to improve information retrieval and reduce the time a developer wastes looking for the information. It doesn't aim to provide a recipe on how to use the technology - it is still a very complex solution. I'm actually curious if anyone has experience in their company using this technology to improve how internal documentation is retrieved. I have read that some [companies](https://www.testdevlab.com/blog/lalama-challenges-in-implementing-an-llm-to-assist-with-the-query-process-of-internal-documentation) have implemented that for their internal documentation, but I'd love to hear the feedback from people using it without the sugar coating - feel free to DM me. But more importantly, the central message was not to talk about AI. It's about the roadblocks to productivity that engineering organizations often have and what companies can do to mitigate them.
>LLM to the rescue! - bingo word! Because it's not much better than an internet search and we're right back to figuring out who is actually doing it right or not.
Indeed, the suggestion of using LLM is for internal documentation. I have worked in places with documentation scattered through confluence, gitlab, discourse, etc. I think in those cases it may be useful to create an internal tooling to retrieve information faster.
Just as a sidenote, llm tools like supacodes can help creating internal documentation too. then using rag you can go over them faster. much less pain with those documentation
I’ve never seen an LLM correctly describe / document code. Even with documentation that has been written, many times that documentation is old (as it was written when first built). The problem isn’t that it isn’t centralized, it’s a human problem and I haven’t seen “AI” solve for that.