Extraction is the use case where the output spec lever from the fundamentals does the heavy lifting. Most failures in production extraction come from a vague schema, not a bad model.

1. Always start with the schema

Define what you want before you write the prompt. A reasonable schema for extracting action items from a meeting:

{
  "action_items": [
    {
      "owner": "string | null",   // person responsible, or null if unstated
      "task": "string",            // short description
      "deadline": "string | null", // ISO date, free-text, or null
      "confidence": "high | medium | low"
    }
  ]
}

A complete schema answers three questions: which fields exist, what types they hold, and what happens when a value is missing. If you cannot answer those, your downstream parser will fail.

2. Use the provider's structured-output mode

OpenAI, Anthropic, Google, and most local-model runtimes now support schema-constrained output. Use it:

# OpenAI-style
response = openai.chat.completions.create(
    model="gpt-4o",
    response_format={"type": "json_schema", "json_schema": {...}},
    messages=[...]
)

The model is mechanically constrained to emit valid JSON matching the schema. No regex, no fallback parser, no "what if the model says 'Here is the JSON:'" anxiety.

3. One worked example beats five paragraphs of rules

Even with a schema, one example pins down the gray areas:

Example input:
"...so I'll talk to legal next week about the new clauses..."

Example output:
{
  "action_items": [
    {
      "owner": "speaker",
      "task": "talk to legal about new clauses",
      "deadline": "next week",
      "confidence": "high"
    }
  ]
}

This example teaches the model what "owner = speaker" means and that "next week" is acceptable as a deadline value rather than being normalized to a date.

4. Be explicit about missing values

The most common production bug in extraction is the model saying "deadline": "none mentioned" instead of "deadline": null. Both are JSON-valid; only one is what your code expects.

Spell it out in the prompt:

For any field where the source does not provide a value:
- Set the field to null. Do NOT use "none", "unknown", or empty string.
- Do not invent or infer values from context.

5. Validate after, always

Even with structured output, validate. Use a library (Pydantic, Zod) to parse the response and fail loudly on schema violations. Treat extraction output as untrusted input to the rest of your system, because it is.

A robust extraction setup is 80% schema, 15% example, 5% prompt wording. The phrasing matters least. If schema-first thinking is unfamiliar territory, it pays to learn the concept first so the schema, not the wording, becomes your default starting point.