I work in Big Tech™. I will tell you that they are certainly not lying. Though maybe they are overstating how much velocity they've gotten. Or at least generously attributing llms to a refinement of their pdlc to maximize the output that llms facilitate.
On hackernews, some people would have you believe that llms are essentially useless, produce only garbage, and literally everyone that says they're productive with them is a liar or has ai psychosis. But it's just simply not the case.
In order to have a serious convo about what you're doing wrong, you'd need to describe your workflow and codebase more.
My experience is that it takes sustained use to build an intuition about how to work with them / maximize the value prop of the llm.
I've also noticed that it really is only an extension of how good of an engineer you are. If you start to step outside of your domain, the llm will amplify that lack of knowledge. If you don't know how to assess and steer the output, then it will produce slop.
My workflow is essentially:
1. apply selective context building. Start the session by pointi g out files and concepts within the codebase that are relevant to the task at hand.
2. At the end of that turn, I'll wrote a file that describes the work that I want accomplished. I'll tell the agent, "read this file, create a plan"
3. Usually the agent will have some questions or miss things so I usually answer the questions in the plan file. I'll revise it myself, and add notes
4. Clear context, execute plan
Afterwards, I encapsulate expected flows into end to end or integration tests that exercise the work. Sometimes the final result needs iterating. I'll also write performance tests or visual regression tests depending on the domain.
There's more, but I'll stop riffing. I've found that the above workflow tremendously improves the speed at which I'm able to iterate and ship.
Google: how many thousands (or millions?) of employees
You: 1 person
Consider the dramatic difference in capacity.
It's uncertain what you are doing wrong.
I work in Big Tech™. I will tell you that they are certainly not lying. Though maybe they are overstating how much velocity they've gotten. Or at least generously attributing llms to a refinement of their pdlc to maximize the output that llms facilitate.
On hackernews, some people would have you believe that llms are essentially useless, produce only garbage, and literally everyone that says they're productive with them is a liar or has ai psychosis. But it's just simply not the case.
In order to have a serious convo about what you're doing wrong, you'd need to describe your workflow and codebase more.
My experience is that it takes sustained use to build an intuition about how to work with them / maximize the value prop of the llm.
I've also noticed that it really is only an extension of how good of an engineer you are. If you start to step outside of your domain, the llm will amplify that lack of knowledge. If you don't know how to assess and steer the output, then it will produce slop.
My workflow is essentially:
1. apply selective context building. Start the session by pointi g out files and concepts within the codebase that are relevant to the task at hand. 2. At the end of that turn, I'll wrote a file that describes the work that I want accomplished. I'll tell the agent, "read this file, create a plan" 3. Usually the agent will have some questions or miss things so I usually answer the questions in the plan file. I'll revise it myself, and add notes 4. Clear context, execute plan
Afterwards, I encapsulate expected flows into end to end or integration tests that exercise the work. Sometimes the final result needs iterating. I'll also write performance tests or visual regression tests depending on the domain.
There's more, but I'll stop riffing. I've found that the above workflow tremendously improves the speed at which I'm able to iterate and ship.
I personally think they're lying, or at least being very generous with the truth.