Model Parameters and Configuration
When you interact with an LLM, you're not just sending a prompt — you're also (often implicitly) setting parameters that control how the model generates its response. Understanding these parameters helps you get more consistent, appropriate outputs for different tasks.
Temperature: Creativity vs Consistency
Temperature is the most important parameter. It controls randomness in the model's word choices.
At each step of generation, the model calculates probabilities for possible next tokens. Temperature affects how it samples from these probabilities:
Low temperature (0.0 - 0.3) — The model strongly favors the most probable tokens. Outputs are deterministic, focused, and consistent. The same prompt produces nearly identical responses.
Medium temperature (0.4 - 0.7) — Balanced creativity and coherence. Good for most general tasks.
High temperature (0.8+) — The model considers less probable tokens more often. Outputs become creative, varied, and sometimes unpredictable.
Temperature guidelines:
0.0 - Code generation, factual answers, data extraction
0.3 - Technical writing, explanations
0.7 - General conversation, balanced tasks
1.0+ - Brainstorming, creative writing, generating alternatives
For coding tasks, lower temperatures usually work better. You want consistent, correct code — not creative interpretations.
Top-p (Nucleus Sampling)
Top-p offers an alternative way to control randomness. Instead of adjusting all probabilities (temperature), it considers only tokens whose cumulative probability reaches a threshold.
With top-p of 0.1, the model only considers the most likely tokens that together have 10% probability. With top-p of 0.9, it considers tokens covering 90% of probability mass.
Most applications use either temperature or top-p, not both. Temperature is more intuitive for most users.
Other Useful Parameters
Max tokens limits response length. Set this to prevent runaway generation and control costs. Too low, and responses get cut off mid-sentence.
Stop sequences are strings that halt generation. Useful for structured output — for example, stopping at triple backticks to end a code block cleanly.
Frequency penalty discourages the model from repeating the same words. Helpful when outputs become repetitive.
Presence penalty discourages returning to topics already mentioned. Useful for brainstorming diverse ideas.
Choosing Parameters for Your Task
Start with defaults and adjust based on results. If code generation produces inconsistent results, lower the temperature. If brainstorming feels repetitive, raise it. Most AI coding tools handle these settings for you, but understanding them helps when you need to customize.
See More
- Writing Effective Prompts for Code
- What Makes a Good Architecture Prompt?
- Building Your AI Coding Workflow