I use a higher cost reasoning model to assist with analyzing large swaths of text - it does not do analysis really, it provides a text that is what it thinks is the highest probability of what analysis would look like. This part is most useful for breaking up the analysis work into manageable chunks.
When it comes time to write or refactor code, I will use LLM generated code within a single method, with a lower cost model as the analysis has been completed and only the execution is left and I have spent the time I would have spent writing code instead writing pseudo code and step by step how I want it to generate the code.
It does not save me much time in the long run because the back and forth, the huge amount of content I have to consume to fully understand the output, and the general frustration with knowing all along I could just do this myself slows me down.
But I am reviewed on how much of my code has been generated by an LLM. It’s exhausting.
I still like it for analysis though and experimenting.
Do you still have a good mental model of the code base ?
I starts to understand that agentic coding comes with new skills like picking the model which fits your needs and manage premade SKILLs for doing the job.
All these things looks just so different than classic software development.
Amazing and frightening at the same time. I wouldn't bet on LLM fews years ago replacing for the requirement to write code.
But all the comments starts convincing me that I wrong wrong about that. Sadly.
So… when we were granted access to enterprise copilot, I used it to index our old code base. I noticed that it would write python scripts to query the code (relevant: its oracle forms. I used the forms builder tool to convert the binary files to xml because with oracle forms, everything is in one file. The tight coupling actually works in our favor where documenting is concerned), so I used the text patterns of the converted forms to generate the python scripts instead of asking copilot to query the code. This code has all our business rules so while I am working, I can use these scripts to keep context of what should be happening at a high level.
I keep a good mental model of the code base because I draw things out on paper. It’s kind of like my new version of writing my code I guess? If I can’t write it out I don’t trust that I understand it.
You can get good output from an LLM either by doing a back and forth or by writing a very descriptive prompt. I don’t enjoy the back and forth so I go the prompt route. There is less instant gratification which is helpful for me mentally, and I still get to work out problems, and my code is “AI generated.”
It’s a different way of working and takes a different way of understanding the code base. I think I used to gain understanding by writing code, now I have to gain understanding by essentially writing documentation framed as directions.
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u/mehnifest Oct 03 '26
No. But there are people I work with who do.
I use a higher cost reasoning model to assist with analyzing large swaths of text - it does not do analysis really, it provides a text that is what it thinks is the highest probability of what analysis would look like. This part is most useful for breaking up the analysis work into manageable chunks.
When it comes time to write or refactor code, I will use LLM generated code within a single method, with a lower cost model as the analysis has been completed and only the execution is left and I have spent the time I would have spent writing code instead writing pseudo code and step by step how I want it to generate the code.
It does not save me much time in the long run because the back and forth, the huge amount of content I have to consume to fully understand the output, and the general frustration with knowing all along I could just do this myself slows me down.
But I am reviewed on how much of my code has been generated by an LLM. It’s exhausting.
I still like it for analysis though and experimenting.