alt Hacker News> The advantage holds while the DSL stays small and constrained enough that a few in-context examples can convey its usage. There is also a real upfront cost in designing and maintaining the language and its semantic model. The payoff is therefore concentrated in well-factored, genuinely constrained DSLs backed by a validator.
dsl stays small is doing all the heavy lifting here
the premise is that because of these few existing dsls (like PlantUML mentioned) my "new dsl" will be equally effective. PlantUML has millions of examples in the training data, my new dsls are not (specially if its not json/yaml or just function chain based). as the number of things that can mix and match increase you are basically looking at a whole system prompt just describing the new language.
this brings us to the second part. step 2: after dsl is 'planned' (note they use the java compiler), the dsl need to have a real compiler/executor, not just a validator. because if then you are going to ask the llm to "compile the dsl to implementation" we are back to square 1.
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I’ve had good luck with LLMs and ad hoc DSLs, as well as much less common DSLs like liquidsoap’s stream management DSL.
I don’t think there’s magic in DSLs, I just think LLMs respond well to clear, simple structure.
Compilation / execution is often true, but not necessary. DSLs can be entirely declarative and used just for gating the stages of a multi-step workflow with checkpoints that have more structure than natural language.
> you are basically looking at a whole system prompt just describing the new language
whats wrong with this? You may be over-indexing on the need for large quantities of examples. These days self-play through RL is far more effective and data (not compute) efficient.
DSLs are a great middle ground for 'use LLM to turn ambiguous spec into something well defined', with the caveat that without discipline they'll inevitably expand until you should just have the agent write whatever the final language is.
There's a context tax up front (which will hopefully be less relevant over time) and then you really need a compiler/linter with helpful errors to keep it on the rails, because there is no corrective context in pretraining for something novel.
A purely descriptive DSL is just a convention, which is useful, but doesn't inject reliability the same way an enforced syntactic contract does.
I just have them write the tools to write the DSL's to do the thing then (most of) the sloppy code stays in the generator and if all the different things depend on each other they don't go stale and whatnot. And let them design the DSL themselves for whatever task so it matches their 'internal concept' of how the things work.
Worked out pretty well so far but not really practical unless your goal is to make the tools to make the DSLs to make jitting VMs -- https://github.com/dan-eicher/BBQ kind of snowballed from "let's parse some binary files" to a way over the top toolkit for playing around with this stuff but, it's fun...
> the premise is that because of these few existing dsls (like PlantUML mentioned) my "new dsl" will be equally effective. PlantUML has millions of examples in the training data, my new dsls are not (*specially if its not json/yaml* or just function chain based)
I can confirm that having a DSL that is json/yaml helps a ton. Kind of like static type checking, it eliminates entire swaths of syntactical errors, allowing the LLM to focus on the semantics.
> because if then you are going to ask the llm to "compile the dsl to implementation" we are back to square 1.
I think this is an edge case; 99% of the time you (and/or the LLM) would have access to the implementor so it wouldn't need to do this.