how dslai works

constrain, don’t train

a grammar already describes your language exactly. instead of hoping a model learns that grammar from examples, dslai uses it as a hard constraint while the model decodes: at every step, only tokens that keep the output inside the grammar are allowed. the result is valid by construction— syntactically wrong output isn’t unlikely, it’s impossible. this is the same idea as GBNF grammars in llama.cpp, Outlines, and XGrammar, and it needs no training data and no GPU to demonstrate. you can watch it work in the playground.

validate with a parser, not a vibe

checking whether a string is valid DSL is a job for a parser, not a language model. dslai generates a deterministic validator from your grammar that returns a precise verdict — valid, or invalid at an exact position with what it expected there. that’s a reproducible compiler-style error you can put in CI, not a confidence score you have to second-guess.

where fine-tuning fits

fine-tuning isn’t useless — it earns its place for hard semanticquality on small, self-hosted open models you want to run cheaply. but it’s an upsell on top of the guarantee, not the foundation, and to be economical it has to be served multi-LoRA (one base model, an adapter swapped in per request) rather than as a dedicated warm GPU per customer. the orchestration runs on Vercel; the heavy compute stays external and pay-per-use.