AI帮你做用户研究？这两大场景超实用！

Core Insights - The article discusses the unprecedented opportunities and challenges in user research in the digital age, emphasizing the role of AI language models in handling vast amounts of user feedback and insights [1] Short Text Feedback Classification - There are two main AI solutions for short text classification, each suited for different scenarios [2] - General model classification acts like a "smart temporary worker," suitable for occasional tasks or early project phases, allowing for flexible categorization without extensive training data [3] - SFT (Supervised Fine-Tuning) model classification is akin to a "custom expert," ideal for stable business scenarios requiring high accuracy, but necessitates significant initial effort in preparing quality labeled data [4][6] Long Text Analysis Insights - Long text analysis involves organizing interview records into a knowledge base, enabling AI to provide comprehensive insights based on user queries [9] - RAG (Retrieval-Augmented Generation) technology enhances information processing efficiency, allowing for quick extraction of insights that would otherwise take hours [10] Efficiency Tips - For effective short text classification, clear instructions and quality labeled data are crucial [7][8] - In long text analysis, proper segmentation of text and optimized retrieval methods are essential for accurate insights [12][13] Conclusion - AI serves as an assistant in user research, improving efficiency while emphasizing the need for human oversight to ensure research quality [11]