My LLM codegen workflow

I believe most people who struggle to be productive with language models simply haven’t put in the necessary practice to communicate effectively with AI. The issue isn’t with the intelligence of the models—it’s that humans are still learning how to use this tool properly. It’s clear that the author has spent time mastering the art of communicating with LLMs. Many of the conclusions in this post feel obvious once you’ve developed an understanding of how these models "think" and how to work within their constraints.

I’m a huge fan of the workflow described here, and I’ll definitely be looking into AIder and repomix. I’ve had a lot of success using a similar approach with Cursor in Composer Agent mode, where Claude-3.5-sonnet acts as my "code implementer." I strategize with larger reasoning models (like o1-pro, o3-mini-high, etc.) and delegate execution to Claude, which excels at making inline code edits. While it’s not perfect, the time savings far outweigh the effort required to review an "AI Pull Request."

Maximizing efficiency in this kind of workflow requires a few key things:

- High typing speed – Minimizing time spent writing prompts means maximizing time generating useful code.

- A strong intuition for "what’s right" vs. "what’s wrong" – This will probably become less relevant as models improve, but for now, good judgment is crucial.

- Familiarity with each model’s strengths and weaknesses – This only comes with hands-on experience.

Right now, LLMs don’t work flawlessly out of the box for everyone, and I think that’s where a lot of the complaints come from—the "AI haterade" crowd expects perfection without adaptation.

For what it’s worth, I’ve built large-scale production applications using these techniques while writing minimal human code myself.

Most of my experience using these workflows has been in the web dev domain, where there's an abundance of training data. That said, I’ve also worked in lower-level programming and language design, so I can understand why some people might not find models up to par in every scenario, particularly in niche domains.

Does the time invested into the planning benefit you? Have you noticed less hallucinations? Have you saved time overall?

I’d be curious to hear because my current workflow is basically

1. Have idea

2. create-next-app + ShadCN + TailwindUI boilerplate

3. Cursor Composer on agent mode with Superwispr voice transcription

I’m gonna try the author’s workflow regardless, but would love to hear others opinions.