Temperature Strategist

The Problem

Most developers use default temperature settings for everything, not realizing that a temperature optimized for creative writing will hallucinate during code generation, and a temperature set for factual accuracy will produce flat, uninspired marketing copy. Understanding temperature is the difference between an AI that generates reliable code and one that invents plausible-looking but broken functions.

The Prompt

I need help choosing the right temperature setting for my task.

TASK TYPE: [describe what you need the AI to do]
PRIORITY: [accuracy vs. creativity — which matters more?]
RISK TOLERANCE: [what happens if the output is wrong or unexpected?]

Based on this, recommend:
1. The optimal temperature value (0.0 to 1.0)
2. Why this temperature fits my task
3. What to watch for at this temperature (common failure modes)
4. A sample prompt optimized for this temperature setting

TEMPERATURE REFERENCE:
- 0.0-0.2: Deterministic (code generation, factual Q&A, data extraction)
- 0.3-0.5: Balanced (technical writing, analysis, structured creativity)
- 0.6-0.8: Creative (marketing copy, brainstorming, storytelling)
- 0.9-1.0: Experimental (poetry, wild ideation, exploring edge cases)

Example Output

TASK: Generate TypeScript interfaces from an API specification
RECOMMENDED TEMPERATURE: 0.1

Why: Interface generation requires strict adherence to the input schema. Higher temperatures
introduce unnecessary type variations (using `any` where a specific type exists, inventing
optional fields, renaming properties). At 0.1, the model follows the spec faithfully with
minimal creative deviation.

Watch for: At very low temperatures, the model may be overly literal — if the spec has
ambiguities, it will not resolve them creatively. You may need to handle edge cases manually.

Optimized prompt at temperature 0.1:
"Generate TypeScript interfaces exactly matching this OpenAPI spec. Use strict types only.
Never use `any`. Mark nullable fields with `| null`, not `?`."

When to Use

Use this skill before starting any AI-assisted task where output quality depends on the right balance of determinism and creativity. It is especially valuable when switching between different task types in the same session — going from code generation to marketing copy requires resetting your mental model of what good output looks like.

Pro Tips

• Match temperature to stakes — production code at 0.0-0.2, draft copy at 0.5-0.7, brainstorming at 0.8-1.0.
• Lower temperature when you have examples — few-shot prompts work best at low temperature because the model should follow the pattern, not riff on it.
• Raise temperature when stuck — if the model keeps giving you the same unsatisfying answer, bump temperature by 0.2 to explore alternative solutions.
• Not all APIs expose temperature — some tools (Claude Code, Copilot) manage it internally; in those cases, use prompt phrasing to simulate temperature effects.