Controlling Model Behavior with Parameters
Experiment with temperature, max_tokens, and top_p to see how they change output style, length, and creativity, then choose appropriate settings for your use case.
The Core Parameters That Matter
A handful of parameters shape your output the most: temperature, max_tokens, top_p, frequency_penalty, and presence_penalty. Master these five and you control the model.
Temperature: Controlling Randomness
Temperature controls randomness. Near 0, the model picks the safest word and stays consistent — great for facts. Around 0.7-1.0, it gets varied and creative. See the code.
from openai import OpenAI
client = OpenAI()
for temp in [0.0, 0.7, 1.5]:
response = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': 'Name a color.'}],
temperature=temp,
max_tokens=5
)
print(f'Temp {temp}: {response.choices[0].message.content}')
# Temp 0.0: Red (always most common)
# Temp 0.7: Blue (varied but sensible)
# Temp 1.5: Vermillion (surprising choices)All lessons in this course
- Setting Up Your Python Environment
- The Chat Completions Endpoint
- Controlling Model Behavior with Parameters
- Error Handling and Rate Limits