Our https://gooey.ai/copilot workflow let's anyone build WhatsApp/FB or their own GPT4 powered chat bots, by just swapping our their own documents and GPT prompts. Additionally, we also enable you to analyze the user's message and bot's answer to create structured data from the raw conversational messages.
Each Gooey.AI copilot will have their own conversational analysis script like this one to help analyze the user's questions and the bot's ability to answer them well.
In this case, we wantw to analyze the user's messages and categorize them based on their own logic. Hence, this LLM prompt takes a message and response and then determines whether:
Run
Examples
API
Variables let you pass custom parameters to your workflow. Access a variable in your instruction prompt with Jinja, e.g. {{ my_variable }} Learn more.
{{ my_variable }}
assistant_msg
user_msg
⚙️ Settings
Avoid Repetition
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The maximum number of tokens to generate in the completion. Increase to generate longer responses.
Higher values allow the LLM to take more risks. Try values larger than 1 for more creative applications or 0 to ensure that LLM gives the same answer when given the same user input.
How many answers should the copilot generate? Additional answer outputs increase the cost of each run.
Run cost = 1 credits
Breakdown: 1Cr for ChatGPT (openai) + 1Cr/run
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ChatGPT (openai)
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