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Understanding top_p in OpenAI API: From Safe to Creative Outputs
🔍 What is top_p?
top_p controls how many tokens (words or pieces of words) the model considers when generating the next word. It uses a technique called nucleus sampling.
top_p: 1➜ The model considers all possible tokens.top_p: 0.8➜ The model only considers the top tokens whose cumulative probability adds up to 80%.
🧠 Example:
Let’s say the model is trying to predict the next word. Here’s a simplified list of possible words and their probabilities:
| Word | Probability |
|---|---|
| happy | 0.4 |
| joyful | 0.3 |
| glad | 0.1 |
| sad | 0.08 |
| upset | 0.05 |
| furious | 0.03 |
| others | 0.04 |
With different top_p settings:
top_p: 1- All of these tokens are included in the selection pool.
- The model can choose even rare, unexpected, or creative words.
- Output is more diverse, but sometimes less predictable.
top_p: 0.8- Only the top tokens (
happy,joyful,glad) are considered. - Output will be more focused, safer, and more expected.
- Only the top tokens (
🤔 top_p vs temperature
| Parameter | What it controls | Typical range | Behavior |
|---|---|---|---|
top_p | Sampling pool (limits candidate tokens) | 0.8–1 | Lower = safer |
temperature | Sampling randomness (how “creative”) | 0.2–1 | Lower = more focused |
📌 Best practice: Change either top_p or temperature, not both at the same time (unless you know what you’re doing).
✅ When to use what?
| Goal | Recommended setting |
|---|---|
| Precise, safe, professional output | top_p: 0.8 |
| Creative, diverse, surprising output | top_p: 0.9–1 |
| Random fun text (e.g. poetry, ideas) | top_p: 1, temperature: 0.9–1.2 |
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