Are you using prompt engineering in your apps? Which parameters do you most use?

1. Context Window The context window parameter determines the amount of text that the model takes into account when generating a response. By adjusting the context window, you can control the level of context the model considers while generating the output. A smaller context window focuses on immediate context, while a larger context window provides a broader context. For example, setting the context window to 100 tokens allows the model to consider the last 100 tokens of input text. Credit : OpenAI Documentation 2. Max Tokens Max tokens parameter defines the maximum number of tokens in the generated response. Tokens can be thought of as the individual units of text, which can be words or characters. By setting the max tokens value, you can limit the length of the generated output. For instance, if the max tokens value is set to 50, the model will generate a response containing a maximum of 50 tokens. 3. Temperature Temperature is a parameter that controls the randomness of the generated output. A higher temperature value, such as 1.0, leads to more randomness and diversity in the generated text. On the other hand, a lower temperature value, like 0.2, produces more focused and deterministic responses. Adjusting the temperature allows you to influence the creativity and exploration of the model. 4. Top P Top P, also known as nucleus sampling or probabilistic sampling, determines the cumulative probability distribution used for sampling the next token in the generated response. By setting a value for top P, you can control the diversity of the output. A higher top P value, for example, 0.9, allows more choices to be considered while sampling, resulting in more diverse responses. Conversely, a lower top P value, like 0.3, limits the choices and generates more focused responses. 5. Top N Top N is another parameter used for sampling the next token, similar to top P. However, instead of using a cumulative probability distribution, top N considers only the top N most likely tokens at each step. By adjusting the top N value, you can manage the diversity of the generated output. A higher top N value, such as 10, allows more options to be considered, resulting in diverse responses. Conversely, a lower top N value, like 3, limits the choices and produces more focused responses. 6. Presence Penalty Presence penalty is a parameter used to discourage the model from mentioning certain words or phrases in the generated response. By assigning a higher presence penalty value, such as 2.0, you can reduce the likelihood of specific words or phrases in the output. This parameter is useful when you want to avoid certain content or bias in the generated text. 7. Frequency Penalty Frequency penalty is another parameter that can be used to control the repetition of words or phrases in the generated output. By setting a higher frequency penalty value, like 1.5, you can penalize the model for repeating the same words or phrases excessively. This helps in generating more diverse and varied responses.