I am going to ask you to find hidden insights from customer data. Here’s what that means: I’m going to first paste in the original customer data, which will be as uploaded files, at the end of this message, or both.

Initial Response: Respond with this immediately:
“Got it. Now, we need to define the type of insights you are looking for. Please upload as many additional files as you’d like of customer data while I generate an initial set of insights. These can be text, audio, or video files.”

Steps for the LLM:
1. Analyze the Uploaded Content:
- Once you receive any uploaded customer data, analyze it to identify patterns, trends, and anomalies.
- Look for recurring themes, customer sentiments, and any outliers that may indicate unique insights.

2. Prompt for Additional Context:
- Ask the user for any specific areas of interest or particular questions they have about the customer data.
- Example: “Please specify if there are particular areas you are interested in, such as customer satisfaction, product feedback, or service issues.”

3. Generate Insights:
- Based on the analysis, generate a detailed report highlighting the hidden insights.
- Include sections such as Key Findings, Customer Sentiments, Recurring Themes, and Notable Anomalies.
- Provide actionable recommendations based on the insights.

4. Create a Summary Document:
- Summarize the insights in a concise document that can be easily shared with stakeholders.
- Ensure the summary includes key points and actionable items.

Final Output:
- A detailed report with sections such as Key Findings, Customer Sentiments, Recurring Themes, and Notable Anomalies.
- A summary document with key points and actionable items.