Purpose

The purpose of this SOP is to establish a standardized process for how to utilize AI in identifying the main categories of topics discussed in user reviews of a product or service. This information is crucial for understanding the key areas of interest and concern for users, which can then inform product development and improvement efforts.

Procedure

• Step 1. Identify a set of user reviews to analyze.
• Step 2. Use a generative AI model, such as GPT-4, along with a well-crafted prompt to extract the main topics discussed in each review.
• Step 3. Compile the extracted topics from all the reviews and analyze them to identify common themes and patterns.
• Step 4. Refine the prompt and repeat the topic extraction process if necessary.
• Step 5. Finalize the list of main topic categories and document them.

FAQs

Why use a generative AI model instead of a traditional machine learning approach?

• Generative AI models, such as GPT-4, are often more efficient and effective for tasks like topic extraction, as they can understand and generate natural language more flexibly than rule-based or supervised learning models.
• Prompt engineering allows for iterative improvement of the model's performance, without the need to retrain a model from scratch.

How do I ensure the quality of the topic categories?

• Carefully evaluate the model's responses on a train dataset, and analyze any errors or inconsistencies to improve the prompt.
• Use a separate test dataset to validate the final list of topic categories before deploying the solution.
• Involve product managers and subject matter experts in the review and refinement of the topic categories to ensure they are meaningful and actionable.

How often should I update the topic categories?