Temperature and Sampling: Controlling AI Creativity
Temperature, top-p, and other sampling parameters control how creative or deterministic AI outputs are. Learn how to tune them.
TL;DR
Temperature and sampling parameters control randomness in AI outputs. Lower temperature = more predictable; higher = more creative. Tune these to balance creativity and consistency.
What is temperature?
Definition:
A parameter (0-2) that controls randomness in text generation.
Low temperature (0-0.3):
- Predictable, consistent
- Picks most likely words
- Good for: Factual answers, code, translations
Medium temperature (0.7-1.0):
- Balanced creativity
- Good for: Writing, brainstorming, chat
High temperature (1.0-2.0):
- Very creative, unpredictable
- Can be incoherent
- Good for: Poetry, creative fiction, wild ideas
How temperature works
- Model predicts probability for each possible next word
- Temperature adjusts these probabilities
- Higher temp = flatter distribution (more randomness)
- Lower temp = sharper distribution (more deterministic)
Other sampling parameters
Top-p (nucleus sampling):
- Limits choices to top X% probability mass
- 0.9 = consider top 90% most likely words
- Alternative to temperature
- Limits to top K most likely words
- K=40 = choose from 40 best options
Frequency penalty:
- Reduces repetition
- Higher = less likely to repeat words
Presence penalty:
- Encourages new topics
- Higher = more diversity
When to use each setting
Factual tasks (temp 0-0.3):
- Data extraction
- Translations
- Code generation
- Structured outputs
Creative tasks (temp 0.7-1.2):
- Writing stories
- Brainstorming
- Marketing copy
Exploration (temp 1.5+):
- Generating many diverse options
- Experimental creative writing
Combining parameters
Deterministic + focused:
- Temperature: 0
- Top-p: 0.1
- Result: Very consistent outputs
Creative + coherent:
- Temperature: 0.9
- Top-p: 0.9
- Frequency penalty: 0.5
- Result: Creative but readable
Common mistakes
- Using high temp for code (breaks syntax)
- Using temp 0 for creative writing (boring)
- Not testing different settings
- Assuming default is always best
Best practices
- Start with defaults
- Adjust based on output quality
- Test systematically
- Document what works for each use case
What's next
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Key Terms Used in This Guide
Parameters
Numbers inside an AI model that get adjusted during training to improve accuracy. More parameters usually mean more capability.
Temperature
A setting that controls how creative or random AI outputs are. Low = predictable and focused. High = creative and varied.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
Fine-Tuning
Taking a pre-trained AI model and training it further on your specific data to make it better at your particular task.
Top-p (Nucleus Sampling)
A parameter that controls randomness in AI text generation by choosing from the smallest set of words whose probabilities add up to p%. Lower values (0.1-0.5) make output more focused; higher values (0.9-1.0) make it more creative.
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