Making LLM features production-ready
A demo that works in the playground and a feature that works in production are different things. The gap is usually two problems: the model occasionally returns output your code can't parse, and the bill climbs faster than the usage as prompts get longer.
dsl·ai uses constrained decoding to force valid, schema-correct output every time, no fine-tuning required. token·flow analyses where your tokens actually go and where compression or caching cuts the cost. One makes the output reliable, the other makes it affordable.
the products
guides
- comparisonConstrained decoding vs fine-tuning: the right way to get valid DSL out of an LLM
- how toHow to make an LLM output valid DSL every time (without fine-tuning)
- use caseValidate your DSL in CI: reject malformed config in a pull request
- use caseFine-tuning an LLM on your DSL: when it's worth it, and when it isn't
- use caseStructured LLM output for your own language, guaranteed rather than hoped for
- comparisonJSON schema vs grammar-constrained decoding: when JSON mode isn't enough for your language
- use caseStop LLM syntax errors: make broken code and DSL impossible by construction
- use caseGenerate a parser from your grammar: valid or invalid, with the exact position it broke
- use caseGrammar-constrained decoding, explained: why the output is valid by construction
frequently asked
How do I guarantee an LLM returns valid structured output?
Constrained decoding restricts the model's next-token choices to only those that keep the output valid against a grammar or schema, so it cannot produce malformed JSON or an invalid DSL in the first place. It's more reliable than prompting-and-hoping or retrying on parse failure.
What's the cheapest way to cut LLM API costs?
Start by measuring: most overspend hides in long, repetitive prompts and uncached repeated calls. Prompt compression removes tokens that don't change the answer, and caching reuses work across similar requests. Together they often cut the bill without touching model quality.