structured output

What separates a demo prompt from a production prompt is structure, versioning, and defense. What matters is treating the system prompt as a contract and templates as parameterized, testable artifacts. Here is the answer.

Complex tasks rarely fit one prompt, and free-text LLM output rarely drops into code cleanly. What matters is decomposing into chained steps and parsing each output. Here is the answer that ties prompting to real pipelines.

Tool calling powers every agent. The signal is grasping that the model executes nothing: it emits a structured request your code runs, then you hand the result back. Here is the loop and the details that determine reliability.

Token count is latency and dollars, not merely style. 'Be concise' hardly works, and max_tokens just cuts off mid-sentence. Here is how to genuinely shape length without amputating answers.

Few-shot accuracy can swing on example order by itself. If your prompt is fragile to things that shouldn't matter, the fix is structural, not lucky example-hunting. Here is what genuinely reduces variance.

Generation has to end somewhere, and the wrong stop rule either truncates answers or wastes tokens on trailing garbage. Here is how the EOS token, stop strings, and max-token caps actually interact.

Agents and integrations depend on the model returning valid, schema-conforming output. The signal is stacking constrained decoding, schema validation, and retries, rather than trusting a prompt to do the job. Here is the production-reliability answer.

The classic recursive-descent exercise with an applied-AI twist: the JSON your model streams stays truncated mid-token for the whole generation. What matters is a clean strict parser plus a small repair layer, not a second parser. The code follows.

The naive version splits on newlines and works right up until a TCP chunk lands mid-line. Then there is the error that arrives after a 200 OK, the user who closes the tab while you keep paying for tokens, and JSON you cannot parse until it closes.

Forcing valid JSON sounds free, but the mask computation can stall every decode step. The fix is precompiling the grammar into a fast automaton. Here is where the cost hides and how to strip it out.