Understanding OpenAI API Parameters

Controlling the behavior of your chat completions starts with setting the right parameters. In this guide, you’ll learn how to influence creativity, length, repetition, and more—both programmatically and via the OpenAI Playground.

Quickstart Example

import os
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")

completion = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user",   "content": "Hello!"}
    ]
)

print(completion.choices[0].message)

This snippet covers:

1. Importing libraries
2. Loading your API key
3. Calling the chat completion endpoint
4. Printing the assistant’s reply

While model and messages are the only required fields, the full API offers many more knobs.

Core Parameters

1. model and messages

• model: Choose an engine (e.g. gpt-3.5-turbo, gpt-4).
• messages: An ordered list of {role, content} objects. Roles include:

2. Randomness & Diversity: temperature vs. top_p

Both control how “creative” the output can be. Adjust one at a time for best results.

• temperature (0–2, default 1):
  • Lower → more focused, deterministic (e.g. 0–0.2)
  • Higher → more creative, random (e.g. 0.8–2)
• top_p (0–1, default 1):
  • Also called “nucleus sampling.”
  • Model only considers tokens whose cumulative probability ≤ top_p.

3. n (number of completions)

Generate multiple replies in one API call. Default is n=1. Raising n can help you compare variations but increases token usage.

4. max_tokens

Limits the length of the completion. Remember:

• prompt_tokens + max_tokens ≤ model context window (e.g. 4096 for gpt-3.5-turbo).

5. presence_penalty vs. frequency_penalty

Prevent the model from repeating itself:

• presence_penalty (–2.0 to 2.0): Encourages new topics by penalizing tokens that have appeared at all.
• frequency_penalty (–2.0 to 2.0): Penalizes tokens proportionally to how often they’ve already appeared.

Interactive Exploration with the Playground

Experiment with all of these settings in real time:

Head over to the OpenAI Playground to tweak parameters and see instant feedback.

Example: Deterministic vs. Creative Runs

Below is a side-by-side comparison showing how temperature and top_p affect output style:

import os
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")

p_model = "gpt-3.5-turbo"
p_messages = [
    {"role": "system", "content": "You are an assistant to write poems"},
    {"role": "user",   "content": "Write a short poem on the Taj Mahal in one stanza."}
]
p_n = 1
p_max_tokens = 100
p_presence_penalty = 0.0
p_frequency_penalty = 0.0

# Deterministic output
completion = openai.ChatCompletion.create(
    model=p_model,
    messages=p_messages,
    n=p_n,
    max_tokens=p_max_tokens,
    temperature=0.0,
    top_p=0.0,
    presence_penalty=p_presence_penalty,
    frequency_penalty=p_frequency_penalty,
)
print("Deterministic →", completion.choices[0].message.content)

# Creative output
completion = openai.ChatCompletion.create(
    model=p_model,
    messages=p_messages,
    n=p_n,
    max_tokens=p_max_tokens,
    temperature=1.5,
    top_p=1.0,
    presence_penalty=p_presence_penalty,
    frequency_penalty=p_frequency_penalty,
)
print("Creative →", completion.choices[0].message.content)

By comparing these runs, you’ll see the poem’s structure and word choice change dramatically.

Summary Table of Key Parameters

Links and References

• OpenAI API Reference
• OpenAI Playground
• OpenAI Python Library

Next up: using these parameters to automate full blog-post generation—stay tuned!