Over the years, I have implemented parsers numerous times, both at work and for side projects, so writing recursive-descent parsers from scratch has become second nature. Once you understand the mechanics, writing a parser by hand is straightforward and offers distinct advantages, particularly much greater flexibility with error handling and reporting. Because of that, I had always viewed PEGs and parser generators as tools primarily for people who couldn’t hand-roll their own because of their circumstances or skill level. (If you look at projects that are neither understaffed nor underskilled you'll find that hand-written recursive-descent parsers are very common: Clang, Go, Rust, TypeScript, Swift and Lua all have hand-written parsers.)

LLMs have changed the equation. It used to take me 2-3 hours to write a parser for a moderately complex grammar. Now, if I hand an LLM a loosely written, BNF-ish grammar and ask for a recursive-descent parser, it finishes the job in five minutes. At this point, writing them by hand is hard to justify. The model does it substantially faster, and lately, often better than I do.

Which makes me wonder: what’s the appeal of PEGs or parser generators now? They used to make sense when hand-writing wasn't practical, but what compelling reasons are left to use them today?

Maintenance when you don't have access to an LLM?

How small of a model can complete the operation you described above? If writing a parser still requires a software forge with 2T of vram and a petabyte of training data, then I still see value in PEGs.

Maybe the smaller local models, given a structured grammar can use a PEG to generate a parser.