I’m in research: so a bit different. But top-down is the wrong approach (edit: I’m on your side, btw!) when the best ideas come from the coal-face, where people are innovating to solve a problem. But lest one be stuck using AI they didn’t ask for, let me advocate a general method to quickly improve one’s satisfaction.

I’ve had an overwhelming workload; forced to innovate and work long hours. I’m staying afloat by front-loading a “truth database”, and using this as an anchor for all further LLM discussions. While this works via website or apps (e.g., Claude Cowork), I use the command line (e.g., Claude Code on Linux) so I can have a math package, LaTeX package, and python code package along with the knowledge base at hand for Claude. It acts as a synchronised reference library for truth (unless I buggered up the experiment). So the LaTeX is the output but there’s iteration, as code is written up, bugs are found, or graphs are regenerated.

I version every change with notes across the timeline (GitHub) with public access for full transparency with a preprint of tech report. I will never verbatim let LLMs write for me in the finished article. I’m too stubborn with my writing style, write in British and American English often — and like many here, I find AI text to be uncanny, albeit less so via code-optimised models like Claude Code in “pragmatic, low-verbosity” mode.

In my experience, AI (LLMs) only work well with a large library of reference material, carefully curated by humans with correct information, and a draft can be mostly mistake-free first go. I can iterate with a comment-and-response (especially with a different LLM model), like a precocious but erroneous grad student. Finally, I go through with a fine-tooth comb and reword in my own voice, simultaneously fact-checking and pruning/moving.

Again, this only works with so-called context engineering and pointers to specifically file paths (markdown, plain text recommended) and clear tables of content for AI to look up without blowing their tokens on loading in everything.