Update model temperature documentation and add explanatory animation (#94)

Author: hannesrudolph

docs/features/model-temperature.md

@@ -2,25 +2,21 @@
 
 Temperature controls the randomness of AI model outputs. Adjusting this setting optimizes results for different tasks - from precise code generation to creative brainstorming. Temperature is one of the most powerful parameters for controlling AI behavior. A well-tuned temperature setting can dramatically improve the quality and appropriateness of responses for specific tasks.
 
-## What is Temperature?
-
-Temperature is a parameter that controls the randomness of the model's predictions. It's typically a value between 0.0 and 2.0, depending on the model.
-
+<img src="/img/model-temperature/model-temperature.gif" alt="Animation showing temperature slider adjustment" width="100%" />
 
-:::info Common Misconceptions About Temperature Specifically Relating to Coding
-Temperature settings in large language models (LLMs) significantly influence coding outputs, primarily by controlling randomness rather than directly affecting code quality. Here are several common misconceptions clarified:
-
-**Lower Temperature Equals Better Code:** A very low temperature (close to zero) leads to deterministic outputs, resulting in predictable, repetitive, and potentially overly simplistic code. It does not inherently improve the quality of solutions.
+## What is Temperature?
 
-**Higher Temperature Generates Higher-Quality Code:** Increasing temperature introduces more randomness, which can lead to novel or creative solutions but also heightens the risk of errors, convoluted logic, or nonsensical variable names. Higher randomness does not equate to improved code quality.
+Temperature is a setting (usually between 0.0 and 2.0) that controls how random or predictable the AI's output is. Finding the right balance is key: lower values make the output more focused and consistent, while higher values encourage more creativity and variation. For many coding tasks, a moderate temperature (around 0.3 to 0.7) often works well, but the best setting depends on what you're trying to achieve.
 
-**Temperature Directly Impacts Coding Accuracy:** Temperature does not directly affect accuracy or correctness of programming logic. The accuracy of code generated by the model is dependent on its training and the clarity of the prompt provided, rather than the randomness introduced by temperature adjustments.
+:::info Temperature and Code: Common Misconceptions
+Temperature controls output randomness, not code quality or accuracy directly. Key points:
 
-**Temperature Zero is Always Ideal for Coding:** While a temperature of zero is beneficial for consistent, repeatable solutions suitable for basic examples or straightforward tasks, it can limit creativity and exploration for more complex coding challenges.
+*   **Low Temperature (near 0.0):** Produces predictable, consistent code. Good for simple tasks, but can be repetitive and lack creativity. It doesn't guarantee *better* code.
+*   **High Temperature:** Increases randomness, potentially leading to creative solutions but also more errors or nonsensical code. It doesn't guarantee *higher-quality* code.
+*   **Accuracy:** Code accuracy depends on the model's training and prompt clarity, not temperature.
+*   **Temperature 0.0:** Useful for consistency, but limits exploration needed for complex problems.
 :::
 
-Ultimately, selecting an optimal temperature setting involves balancing deterministic responses and creative exploration. Moderate temperatures, typically ranging from 0.3 to 0.7, often offer the best balance for many coding scenarios, though ideal settings may vary based on specific task requirements and desired outcomes.
-
 ## Default Values in Roo Code
 
 Roo Code uses a default temperature of 0.0 for most models, optimizing for maximum determinism and precision in code generation. This applies to OpenAI models, Anthropic models (non-thinking variants), LM Studio models, and most other providers.