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Currently each component is optimized to get the best results from the autoregressive language model based on experience. The prompt and temperature being the most important, length if the app is dependent(think SEO meta description). Example:
openai.createCompletion("text-davinci-002", {
prompt: `Write an uplifting and positive Blog intro paragraph for the blog title ${formDataObj.blogTitle} and include the keywords: ${formDataObj.context}`,
temperature: 0.85,
max_tokens: 200,
top_p: 1,
frequency_penalty: 0,
presence_penalty: 0,
})
On existing components experiment with an option to click "more options" or "advanced options" and allow users to change:
temperature: ideal range for most would be .65-.80 (convert that from 0-100 to make it natural in terms of ux).
max-tokens: set a variable between 20-500 max? (will need to test to find range that produced quality output)
frequency and penalty need exploration (higher presence would be good for idea generation, but need to test ranges to find finite examples)
The text was updated successfully, but these errors were encountered:
Currently each component is optimized to get the best results from the autoregressive language model based on experience. The prompt and temperature being the most important, length if the app is dependent(think SEO meta description). Example:
On existing components experiment with an option to click "more options" or "advanced options" and allow users to change:
The text was updated successfully, but these errors were encountered: