AI Prompt Engineering · Lesson

Choosing Parameters for Your Use Case

Recommended settings for factual Q&A, creative writing, code, and chat.

The Parameter Decision Framework

Choosing sampling parameters is not guesswork — it follows a logic based on your task requirements. Two key dimensions determine the right configuration:

Output accuracy: how important is it that the output is correct and predictable?
Output diversity: how important is it that outputs vary and explore?

High accuracy → low temperature. High diversity → high temperature. Most tasks sit somewhere between these extremes.

Factual Q&A: Accuracy is Everything

For factual question answering, there is typically one correct answer. Any randomness increases the chance of a wrong answer. Use:

temperature = 0: greedy decoding, fully deterministic
top_p = 1.0: leave unrestricted; at temperature=0, top-p has no effect

This ensures the model always picks its most confident answer, which is the most likely to be correct.

import openai
client = openai.OpenAI(api_key='sk-...')

# Factual Q&A configuration
def factual_qa(question):
    resp = client.chat.completions.create(
        model='gpt-4o',
        messages=[
            {'role': 'system', 'content': 'Answer factual questions concisely and accurately.'},
            {'role': 'user', 'content': question}
        ],
        temperature=0,
        top_p=1.0
    )
    return resp.choices[0].message.content

answer = factual_qa('What is the boiling point of water at sea level?')
print(answer)  # Always: '100 degrees Celsius (212 degrees Fahrenheit).'

All lessons in this course

← Back to AI Prompt Engineering