AI without the hype: using LLMs to reduce noise, not replace thinking
The article discusses the author's approach to using AI, specifically large language models (LLMs), to enhance their app review management tool, AppReviews, without letting AI become the product itself.
Why it matters
This approach demonstrates how AI can be effectively integrated into a product to enhance user experience without becoming the primary focus, which is an important consideration for AI-powered applications.
Key Points
- 1The author wanted to use AI to help understand and process a large number of app reviews, without generating summaries or insights that would replace human thinking.
- 2The first step was to use embeddings to group similar reviews and detect recurring topics, without relying on keywords or star ratings.
- 3The author then added an optional layer using a local LLM (llama3.1:8b) to estimate sentiment, extract high-level topics, detect tone, and flag urgency, but without any long context or orchestration complexity.
- 4The entire AI-powered pipeline is optional, so the tool can still function without the AI components.
Details
The author built AppReviews to help manage the influx of app reviews and feedback. As the number of reviews grew, the author realized that simply reading through them all was not scalable. They wanted to use AI to help process the reviews more efficiently, but without letting the AI become the focus of the product. The key was to use AI as a tool to reduce cognitive load, not to replace human thinking. The author started with embeddings to group similar reviews and detect recurring topics, which provided value even without a large language model. They then added an optional layer using a local LLM (llama3.1:8b) to perform specific tasks like sentiment analysis, topic extraction, and urgency detection, but without any long-context or orchestration complexity. The entire AI-powered pipeline is optional, so the tool can still function without the AI components, ensuring that the AI remains a supporting tool rather than the main focus of the product.
No comments yet
Be the first to comment