Ensuring Deterministic Contact Extraction with Azure OpenAI gpt‑5.2

Summary

Azure OpenAI’s gpt‑5.2 chat model is producing inconsistent and incomplete contact extraction results. The output varies between runs (sometimes 1 contact, sometimes 3), and the temperature setting is unavailable, making it hard to control determinism.

Root Cause

Stateless prompting – the model is asked to extract in a single turn, leading it to sample different answers each time.
No temperature control – Azure’s interface for gpt‑5.2 locks the temperature, so the randomness cannot be limited.
Model capacity vs. task complexity – gpt‑5.2 prioritizes broad reasoning, which is unnecessary for a straightforward extraction task.
Prompt ambiguity – the prompt may not explicitly instruct the model to return all contacts, so it interprets differently.

Why This Happens in Real Systems

Stateless API calls mean each request is independent; the same prompt can yield different completions.
Randomness is baked into the model unless explicitly constrained, causing non‑repeatable outputs.
Feature restrictions (e.g., temperature locked) prevent developers from fine‑tuning behavior.
Complex models sometimes focus on higher‑order reasoning, allocating tokens away from simple extraction.

Real-World Impact

Inconsistent data quality: downstream pipelines fail due to missing contact records.
Increased manual review: data engineers must verify and reconstruct missing entries.
Higher costs: repetition of calls results in wasted compute credits.
Reduced developer confidence: unpredictable outputs discourage adoption of AI in core workflows.

Example or Code (if necessary and relevant)

(No code required for this issue.)

How Senior Engineers Fix It

Switch to a specialized extraction model (e.g., text-davinci-003 or gpt‑4o-mini) that offers temperature control and is tuned for factual extraction.

Wrap the extraction in a prompt that explicitly enumerates all contacts:

Extract every phone number, email, and name present in the following text.  
Return a JSON array with object fields: name, email, phone.

Add a post‑processing validator that verifies the result against expected patterns and retries if incomplete.
Use a deterministic temperature (e.g., 0.0) to enforce reproducibility.
Cache the prompt and feed a consistent prompt version to avoid inadvertent drift.

Why Juniors Miss It

Assuming randomness is acceptable without understanding temperature settings.
Overlooking model selection and using the latest model by default, ignoring task‑specific suitability.
Missing explicit prompt design; they may not realize how a poorly framed prompt leads to partial data.
Underestimating the need for validation; junior engineers often skip post‑processing checks because the output looks correct at first glance.