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.
Controlling Model Behavior with Parameters is a free AI Engineering Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Engineering Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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)max_tokens: Controlling Response Length
max_tokens caps how long the reply can get — a safety limit, not a target. Too low and answers get cut off; too high wastes money and time. Add a buffer and watch finish_reason.
# Different max_tokens for different use cases
# Classification: short answer expected
classification_response = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': 'Is this positive or negative? "Great product!"'}],
max_tokens=5 # Only need 1-2 words
)
# Detailed analysis: longer output needed
analysis_response = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': 'Analyze the pros and cons of microservices.'}],
max_tokens=800 # Need space for detailed explanation
)top_p: Nucleus Sampling
top_p is another randomness dial: it samples only from the top tokens that add up to top_p of the probability. Tip: tune either temperature or top_p, not both at once.
# top_p usage example
response_narrow = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': 'Continue: The sky is...'}],
top_p=0.1, # only very likely tokens (conservative, predictable)
temperature=1.0 # keep temperature at 1 when using top_p
)
response_wide = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': 'Continue: The sky is...'}],
top_p=0.95, # most tokens eligible (creative, varied)
temperature=1.0
)Frequency Penalty: Reducing Repetition
frequency_penalty discourages the model from repeating the same words, scaling with how often they appear. Reach for it when long answers get repetitive — try 0.3 to 0.7.
# Frequency penalty to reduce repetition in long outputs
response = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{
'role': 'user',
'content': 'Write 5 tips for better sleep.'
}],
max_tokens=300,
frequency_penalty=0.5 # reduces repeating the same words/phrases
)
print(response.choices[0].message.content)Presence Penalty: Encouraging Topic Diversity
presence_penalty nudges the model toward new topics: it penalizes any word that already appeared, even once. Great for brainstorming where you want fresh, varied ideas.
# Presence penalty for diverse brainstorming output
response = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{
'role': 'user',
'content': 'List 10 creative ways to use AI in a small business.'
}],
max_tokens=400,
presence_penalty=0.8 # encourages introducing different topics per item
)
print(response.choices[0].message.content)stop Sequences: Custom Stopping Points
The stop parameter lists strings that halt generation the moment they appear (and they're left out). Stop at a newline to grab a clean single-line answer. See the code.
# Use stop sequences to get clean single-line output
response = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{
'role': 'user',
'content': 'What is the Python keyword for a function definition?\nAnswer:'
}],
max_tokens=20,
stop=['\n', '.'] # stop at newline or period - gets just the keyword
)
print(repr(response.choices[0].message.content)) # 'def'seed: Reproducible Outputs
The seed parameter makes output reproducible: same seed plus temperature 0 gives the same reply. Great for testing — just watch the system_fingerprint for backend changes.
# Reproducible output with seed parameter
response1 = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': 'Pick a random number from 1 to 10.'}],
temperature=0,
seed=42
)
response2 = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': 'Pick a random number from 1 to 10.'}],
temperature=0,
seed=42
)
print(response1.choices[0].message.content) # same
print(response2.choices[0].message.content) # same
print('Fingerprint:', response1.system_fingerprint)Choosing Parameters for Common Use Cases
Skip the guesswork with quick presets: temperature 0 for classification, 0.2 for factual Q&A, 0.8-1.0 for creative writing. Start there, then adjust to your task. See the code.
# Parameter presets for different task types
PRESETS = {
'classify': {'temperature': 0, 'max_tokens': 20},
'factual_qa': {'temperature': 0.2, 'max_tokens': 400},
'creative': {'temperature': 0.9, 'max_tokens': 1000, 'frequency_penalty': 0.3},
'code': {'temperature': 0.1, 'max_tokens': 2000},
'summary': {'temperature': 0.3, 'max_tokens': 300, 'frequency_penalty': 0.2},
}
def complete(prompt, task_type='factual_qa', **overrides):
params = {**PRESETS[task_type], **overrides}
return client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': prompt}],
**params
)Logprobs: Understanding Model Confidence
logprobs returns how confident the model was in each token, plus alternatives it weighed. Low confidence on a fact is a hallucination warning — useful for safer systems.
import math
response = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': 'The capital of France is?'}],
max_tokens=3,
logprobs=True,
top_logprobs=3 # show top 3 alternative tokens at each position
)
for token_log in response.choices[0].logprobs.content:
prob = math.exp(token_log.logprob) # convert log prob to probability
print(f'Token: {token_log.token!r} | Probability: {prob:.2%}')
for alt in token_log.top_logprobs:
print(f' Alt: {alt.token!r} -> {math.exp(alt.logprob):.2%}')Testing Parameter Effects Systematically
Don't guess parameters — test them. Build a small grid search that runs the same prompt across combos and scores each. That turns tuning from art into engineering. See the code.
from itertools import product
# Systematic parameter grid search
temperatures = [0.0, 0.3, 0.7]
max_tokens_options = [100, 300]
test_prompt = 'Summarize the benefits of unit testing in 2 sentences.'
results = []
for temp, max_tok in product(temperatures, max_tokens_options):
resp = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': test_prompt}],
temperature=temp,
max_tokens=max_tok
)
results.append({
'temperature': temp,
'max_tokens': max_tok,
'output': resp.choices[0].message.content,
'actual_tokens': resp.usage.completion_tokens
})Quick Check
Test your understanding of AI Engineering concepts from this lesson.
Lesson Recap
You learned to steer the model: temperature sets randomness, max_tokens caps length (watch finish_reason!), and the penalties cut repetition and add variety. Next: error handling.
Frequently asked questions
Is the “Controlling Model Behavior with Parameters” lesson free?
Yes — the full text of “Controlling Model Behavior with Parameters” is free to read here on the web, and the AI Engineering Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Engineering Academy course, upgrade to CoddyKit PRO.
What will I learn in “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. You practise AI Engineering Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start AI Engineering Academy?
No prior experience is required. AI Engineering Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Controlling Model Behavior with Parameters” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this AI Engineering Academy lesson?
Yes. Every AI Engineering Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Setting Up Your Python Environment
- The Chat Completions Endpoint
- Controlling Model Behavior with Parameters
- Error Handling and Rate Limits