Get started with Azure OpenAI Service

Completions Playground parameters

There are many parameters that you can adjust to change the performance of your model:

• Temperature: Controls randomness. Lowering the temperature means that the model produces more repetitive and deterministic responses. Increasing the temperature results in more unexpected or creative responses. Try adjusting temperature or Top P but not both.
• Max length (tokens): Set a limit on the number of tokens per model response. The API supports a maximum of 4000 tokens shared between the prompt (including system message, examples, message history, and user query) and the model's response. One token is roughly four characters for typical English text.
• Stop sequences: Make responses stop at a desired point, such as the end of a sentence or list. Specify up to four sequences where the model will stop generating further tokens in a response. The returned text won't contain the stop sequence.
• Top probabilities (Top P): Similar to temperature, this controls randomness but uses a different method. Lowering Top P narrows the model’s token selection to likelier tokens. Increasing Top P lets the model choose from tokens with both high and low likelihood. Try adjusting temperature or Top P but not both.
• Frequency penalty: Reduce the chance of repeating a token proportionally based on how often it has appeared in the text so far. This decreases the likelihood of repeating the exact same text in a response.
• Presence penalty: Reduce the chance of repeating any token that has appeared in the text at all so far. This increases the likelihood of introducing new topics in a response.
• Pre-response text: Insert text after the user’s input and before the model’s response. This can help prepare the model for a response.
• Post-response text: Insert text after the model’s generated response to encourage further user input, as when modeling a conversation.

Plan a Responsible GenAI solution

1. Identify potential harms that are relevant to your planned solution.
2. Measure the presence of these harms in the outputs generated by your solution.
3. Mitigate the harms at multiple layers in your solution to minimize their presence and impact, and ensure transparent communication about potential risks to users.
4. Operate the solution responsibly by defining and following a deployment and operational readiness plan.