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A chatty paragraph is nice for humans but painful for code. To build apps, you want structured output you can parse reliably. 🧱

The simplest fix is to ask the model to reply as JSON, naming the exact fields you expect in the answer.

Be precise about the schema: list every key, its type, and allowed values. Ambiguity is what makes parsing break later.

Many APIs offer a JSON mode that forces valid JSON, so the model cannot wrap the answer in chatty prose.

Always wrap parsing in try/except. Even good models occasionally slip, and your code should fail gracefully when they do.

Parsing is not enough. Validate that required keys exist and values are in range before you trust the data downstream.

A Pydantic model defines your fields once and validates the parsed output automatically, raising a clear error on bad data.

Guardrails are the checks around a model that keep it on track: validation, retries, and limits on what it may output.

If validation fails, send the error back and ask the model to fix its output. One retry resolves most small mistakes.

For category tasks, tell the model the exact allowed labels. A closed list stops it from inventing new categories.

Ask the model to say it does not know rather than guess. A clear fallback beats a confident but wrong answer.

What should you do right after parsing an LLM's JSON output?

Ask for JSON, define a schema, parse safely, then validate. Add guardrails like retries and closed label lists to stay reliable. ✅