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El Bruno for Microsoft Azure

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#Azure – #OpenAI Services, Completion Playground Parameters


In part of my demos / explanations on Prompt Engineering, I use Azure OpenAI Services, and the Azure OpenAI Studio.

Note: You can request access to Azure OpenAI Services here:

In the Completion Playground there are several parameters that we can use to change the performance of our model.

These are the ones I usually explain:

How “creative” the model should be


  • Temperature controls randomness in the model’s responses.
  • Lowering the temperature makes the responses more repetitive and predictable.
  • Increasing the temperature makes the responses more creative and unexpected.
  • Adjusting the temperature can be done independently of adjusting Top P.

Top probabilities (Top P):

  • Top P is another way to control randomness in the model’s responses.
  • Lowering Top P limits the model’s token selection to more likely tokens.
  • Increasing Top P allows the model to choose from tokens with both high and low likelihood.
  • Adjusting Top P can be done independently of adjusting temperature.

How to control the token usage

Max length (tokens):

  • Max length sets a limit on the number of tokens in the model’s response.
  • The API supports a maximum of 4000 tokens for both the prompt and the response.
  • One token is roughly equivalent to four characters in typical English text.

Stop sequences:

  • Stop sequences allow you to specify where the model should stop generating tokens in a response.
  • You can define up to four sequences as stop points.
  • The returned text won’t include the stop sequences.

How to add Pre and Post text to the output

Pre-response text:

  • Pre-response text is inserted after the user’s input and before the model’s response.
  • It helps provide context or additional information to the model for generating a response.
  • The inserted text prepares the model to better understand and address the user’s query.

Post-response text:

  • Post-response text is inserted after the model’s generated response.
  • It encourages further user input and promotes a conversational flow.
  • By adding text after the response, you can guide the user to provide more information or continue the conversation.

There are other parameters and other options to change the behavior of a model. However, these are the ones that I mostly explain and use on live prompting demos.

Happy coding!


El Bruno

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