structured output

The difference between a demo prompt and a production prompt is structure, versioning, and defense. The signal 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 plugs into code cleanly. The signal is decomposing into chained steps and parsing each output. Here is the answer that connects prompting to real pipelines.

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

Token count is latency and dollars, not just style. 'Be concise' barely works, and max_tokens just truncates mid-sentence. Here is how to actually shape length without amputating answers.

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

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

Agents and integrations live or die on the model returning valid, schema-conforming output. The signal is layering constrained decoding, schema validation, and retries, not hoping a prompt is enough. Here is the production-reliability answer.

The classic recursive-descent exercise with an applied-AI twist: the JSON your model streams is truncated mid-token for the entire generation. The signal is a clean strict parser plus a small repair layer, not a second parser. Here is the implementation.

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 remove it.