Why am I getting inconsistent extraction results from my model API?

Inconsistent extraction results usually come from one or more of these factors:

1. Model randomness / sampling settings

   • If temperature is above 0, outputs can vary.
   • Higher top_p, top_k, or other sampling controls can also increase variability.
   • For extraction tasks, use low or zero temperature when possible.

2. Prompt ambiguity

   • If the instructions aren’t very specific, the model may interpret the task differently across runs.
   • Missing examples, unclear field definitions, or vague formatting requirements often cause drift.

3. Input variation

   • Small differences in the source text, formatting, whitespace, OCR errors, or truncated content can lead to different extractions.
   • Make sure the exact input is the same each time.

4. Non-deterministic backend behavior

   • Even with the same settings, some hosted APIs can still produce slightly different results due to model updates, load balancing, or infrastructure changes.
   • If the provider silently upgrades the model version, outputs may change.

5. Context window issues

   • If the input is long and near the context limit, the model may lose earlier details.
   • Truncation or partial context can cause inconsistent field extraction.

6. Output schema not enforced

   • If the model is free-form generating text, it may vary in structure.
   • Use structured output features if available:
     • JSON schema / function calling / tool calling
     • strict response format
     • validation with retries

7. Post-processing differences

   • Sometimes the inconsistency comes from your parsing code rather than the model.
   • Different JSON parsers, regexes, or cleanup logic can make the same response look different.

How to make extraction more stable

• Set temperature = 0
• Use a fixed model version if the API supports it
• Use structured outputs or function calling
• Make the prompt explicit:
  • define each field
  • specify allowed null/empty behavior
  • provide a few examples
• Validate output and retry on invalid format
• Keep input normalization consistent

Best practice for extraction

If your task is extraction rather than generation, treat it like a parsing problem:

• give the model a strict schema
• ask for only the fields you need
• forbid extra text
• validate the result programmatically

If you want, I can help you diagnose this more specifically if you share:

• the model name
• your prompt
• your API parameters
• a couple of example inputs/outputs