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.
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, not 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
- how toHow to reduce your OpenAI API costs without breaking your product
- use caseLLM cost optimization for startups: keep the AI bill from eating your runway
- how toPrompt compression: send fewer tokens without losing the answer
- how toCaching LLM responses: stop paying twice for the same answer
- use caseUnderstanding your LLM bill: what you're actually paying for
- use caseSigns your LLM usage is inefficient — and how to fix each one
- comparisontoken·flow vs. tracking LLM costs by hand in a spreadsheet
- use caseThe hidden costs of large language models — and how to find them
- use caseMaximizing LLM ROI: it's not just about a cheaper model
- use caseAI web testing for QA teams: write the test in English, let an agent run it
- how toHow to write natural-language E2E test scenarios an agent can run reliably
- comparisonAgentic testing vs Selenium and static scripts: where each one wins
- use caseThe cost of manual E2E testing, and where an AI agent actually helps
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.