Introduction

Constraints ensure that a model’s response follows specific rules, while parameters such as temperature and max tokens steer the model’s sampling behavior.

• Definition: Prompt constraints are explicit instructions; parameters are settings you pass to the API to influence generation.
• Why: Together they reduce randomness, enforce formatting, and prevent policy violations.
• Where: Enterprise copilots, reporting assistants, and any workflow needing predictable structured output.

Syntax

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    temperature=0.2,           # lower temperature = deterministic output
    max_tokens=350,            # hard stop to control cost
    response_format={"type": "json_object"},
    messages=[
        {"role": "system", "content": "Return JSON with fields: summary, next_steps, tone."},
        {"role": "user", "content": "Summarize the incident report and suggest mitigation actions."}
    ]
)
print(response.choices[0].message.content)

Example

[
  {"role": "system", "content": "Answer in Markdown with headings: Context, Analysis, Recommendation."},
  {"role": "user", "content": "Audit yesterday's deployment log and report any anomalies."}
]

## Context
Deployment v3.4.2 completed on 2025-02-10 at 22:10 UTC.

## Analysis
- Warning: Elevated error rate on payment-service (3.1%).
- Alert: Missing canary verification for checkout pipeline.

## Recommendation
1. Roll back payment-service to v3.4.1.
2. Trigger canary suite before reopening traffic.
3. Record incident in the change log.

def generate_with_temp(temp: float) -> str:
    completion = client.chat.completions.create(
        model="gpt-4o-mini",
        temperature=temp,
        messages=[
            {"role": "system", "content": "Answer with three concise product taglines."},
            {"role": "user", "content": "Launch messaging for a privacy-first email app."}
        ]
    )
    return completion.choices[0].message.content

for t in (0.0, 0.5, 0.9):
    print(f"Temperature {t}:\n{generate_with_temp(t)}\n")

Explanation

• System message defines mandatory structure; combining with response_format keeps output machine-readable.
• Temperature controls randomness; use low values for policy or finance workflows, higher for ideation.
• Max tokens ensures the reply does not exceed a safe length, guarding against runaway costs.
• Top_p and frequency penalties provide additional levers to curb repetition or encourage novelty.

Real-World Use Case

A managed services team delivers nightly compliance summaries to banks. The pipeline sets temperature=0.1, caps tokens at 400, and requires JSON with signed-off fields. This guarantees predictable downstream parsing for dashboards and archiving.

Key Notes / Tips

• Combine constraints with schema validation to reject malformed responses.
• Document default parameter values so all squads use consistent baselines.
• Beware of overly tight constraints—they can make the model respond with errors or apologies.
• Monitor token usage after each change; stricter formats can increase output length.
• When chaining models, pass both the constraints and the actual parameter configuration for observability.

Practice Exercise

1. Write a prompt + parameter set that generates a JSON list of marketing bullet points with a maximum of five items.
2. Tune temperature and top_p for a creative writing assistant; record how tone changes.
3. Challenge: Build a guardrail that rejects responses not matching your JSON schema, then retry with clarified instructions.

Summary

Constraints and parameters are the levers that transform a general-purpose model into a dependable teammate. Set both with intention, log the configuration, and iterate based on feedback from users and monitoring tools.