Claude AI built me a React app to compare maps side by side

Or you're using skis on gravel. I'm a firm believer that the utility varies greatly depending on the tech stack and what you're trying to do, ranging from negative value to way more than 5x.

I also think "prompting" is a misrepresentation of where the actual skill and experiences matter. It's about being efficient with the tooling. Prompting, waiting for a response and then manually copypasting line by line into multiple places is something else entirely than having two LLMs work in tandem, with one figuring out the solution and the other applying the diff.

Good tooling also means that there's no overhead trying out multiple solutions. It should be so frictionless that you sometimes redo a working solution just because you want to see a different approach.

Finally, you must be really active and can't just passively wait for the LLM to finish before you start analyzing the output. Terminate early, reprompt and retry. The first 5 seconds after submitting is crucial and being able to take a decision just from seeing a few lines of code is a completely new skill for me.

I give more details of one instance of this behavior using Claude 3.5 Sonnet a few weeks ago here[1]. I was asking it to implement a specific feature using a popular Go CLI library. I could probably reproduce it, but honestly can't be bothered, nor do I wish to use more of my API credits for this.

Besides, why should I have to prove anything in this discussion? We're arguing based on good faith, and just as I assume your experience is based on positive interactions, so should you assume mine is based on negative ones.

But I'll give you one last argument based on principles alone.

LLMs are trained on mountains of data from various online sources (web sites, blogs, documentation, GitHub, SO, etc.). This training takes many months and has a cutoff point sometime in the past. When you ask them to generate some code using a specific library, how can you be sure that the code is using the specific version of the library you're currently using? How can you be sure that the library is even in the training set and that the LLM won't just hallucinate it entirely?

Some LLMs allow you to add sufficient context to your prompts (with RAG, etc.) to increase the likelihood of generating working code, which can help, but still isn't foolproof, and not all services/tools allow this.

But more crucially, when you ask it to do something that the library doesn't support, the LLM will never tell you "this isn't possible" or "I don't know". It will instead proceed to hallucinate a solution because that's what it was trained to do.

And how are these state-of-the-art coding LLMs that pass all these coding challenges capable of producing errors like referencing an undefined variable? Surely these trivial bugs shouldn't be possible, no?

All of these issues were what caused me to waste more than an hour fighting with both Claude 3.5 Sonnet and GPT-4o. And keep in mind that this was a fairly small problem. This is why I can't imagine how building an entire app, using a framework and dozens of libraries, could possibly be more productive than doing it without them. But clearly this doesn't seem to be an opinion shared by most people here, so let's agree to disagree.

[1]: https://news.ycombinator.com/item?id=41987474