How Farmer.CHAT turns conversations to structured data

Our workflow let's anyone build WhatsApp/FB or their own GPT4 powered chat bots, by just swapping out 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, our Farmer.CHAT partner wants 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:

  1. We should update the user's profile object (because they mentioned their location, crop or gender)
  2. The user's question was answered
  3. The topic of the user's question.

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⌥ Variables

Run cost = 2 credits

Breakdown: 1Cr for ChatGPT (openai) + 1Cr/run

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