Reliable LLM JSON for Production Automations: A Playbook
A repeatable approach to machine-readable LLM output using low temperature, consumer-driven schemas, explicit defaults, validation, and retries.
Forcing structured JSON out of LLMs in production automations
1. Turn down the temperature when the output feeds machinery
2. Design the schema for the consumer, not the model
Separate named fields, not one blob of prose. The analysis step returned each driver as its own field so every placeholder in the downstream document had exactly one thing to bind to.
Explicit defaults. Every field that could be empty returned an N/A default instead of being omitted. A missing key breaks a template; a present N/A doesn't.
The single biggest reliability win is shaping the JSON around whatever consumes it downstream. In practice that meant:
The same principle drove a WhatsApp support agent, where the contract was a two-field object: message (the customer-facing text) and handoff (either NO-HANDOFF or a short reason a human is needed). A decision node then branches on handoff. When the schema is designed for the consumer, the consumer's logic collapses to reading a field instead of parsing sentences.
3. Validate, then retry on malformed output
4. Template the output and guard every binding
5. Prefer structured extraction at the source
The playbook, distilled
- Temperature 0 whenever the output feeds machinery instead of a human reader.
- Separate fields + explicit defaults, designed around the downstream consumer, not the model.
- A fixed contract you branch on — e.g. a
handoffflag — instead of interpreting prose. - Validate and retry on malformed JSON so a bad response never reaches the next stage.
- Template the artifact and guard every binding, and kill placeholder leakage.
- Ask for structure at the source so you're never regex-parsing model prose in the first place.
The model is the easy part. The reliability lives in the schema you demand, the validation you enforce, and the templates you bind into. Treat the LLM as an unreliable service behind a strict contract, and it will behave like a dependable one.
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