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Language Model: ChatGPT-4 Data Analyst Summary: Using synthetic data, analyze customer service emails, chat logs, and product reviews, then compile a list of 10 most frequently asked questions and concerns related to product specifications, order tracking, return policies, and care instructions. Prompting techniques used: Directive Prompting: Give precise instructions to generate specific types of data (customer service emails, chat logs, product reviews), the quantity of data, and the context, then save the generated data to a file for download. Multi-Source Data Synthesis: Synthesize information from the three different types of data sources to gain a well-rounded view of customer concerns. Data-Driven Analysis: Analyze and identify patterns in the data as the basis for creating a targeted list of the most common questions and concerns on the business's website. View FAQs Generation Prompt
Language Model: ChatGPT-4 Data Analyst Summary: Using synthetic data for 1,000 customers, analyze customer segmentation by demographics (age, gender, income), behavioral data (purchase history, browsing patterns), and psychographics (interests, values, lifestyle). Identify segments with the highest lifetime value, pinpoint segments at risk of leaving (churn), and identify customers who are high-value and at-risk. Prompting techniques used: Chain-of-Thought (CoT): Guide the language model to reason through each type of data (demographics, behavioral, psychographics) to categorize customers into segments. For each segment, the model calculates metrics like average lifetime value and churn risk. Few-shot learning: Provide the model with examples of how customer data is analyzed to derive insights about customer segments. This includes examples of how to calculate lifetime value and churn risk based on behavioral patterns and demographic data. Visualization Generator Pattern: Enhance understanding and decision-making by instructing the model to generate textual descriptions for data visualization. This includes descriptions for creating bar charts of customer lifetime value by segment or heat maps of engagement levels. View Customer Segmentation Prompt
Language Model: Claude 3 Opus Summary: Generate a detailed, three-day Paris itinerary in June tailored for young adult travelers interested in art and culture, including specific activities, times, locations, tips, dining suggestions, budget and luxury options, transportation, safety information, and interactive social media challenges to engage the demographic. Prompting techniques used: Chain of Thought (CoT): Guide the model to consider and include all necessary details for each activity in a step-by-step manner to create a comprehensive, logical, and well-structured itinerary that is easy to follow, practical, and tailored to the needs of young adult travelers. Few-shot: Generate creative and engaging activities consistent with the interests of young adult travelers by using examples of engagement strategies, resulting in an itinerary that actively involves and entertains the target audience while enhancing the overall travel experience. Structured prompting: Provide a clear and specific framework enabling the model to generate a consistent, comprehensive, user-friendly itinerary that is well-organized, easy to navigate, and includes all relevant information to plan each day in Paris effectively. View Paris Travel Itinerary Prompt