🎯 Purpose & Audience
This article is aimed at product managers and UX professionals, offering a practical guide to integrating AI—especially large language models (LLMs)—into product development, with a focus on understanding both capabilities and limitations.
🔧 The AI Toolbox
The core of the article is a breakdown of five practical AI techniques, treated as tools to be combined in product pipelines:
• Core method: send text, get text back.
• Use cases: chatbots, summarization, autocomplete, naming, prototyping.
• Caution: prone to hallucination and unpredictable outputs.
• Tools: DALL·E, Imagen.
• Use cases: avatars, illustrations, visual helpers.
• Challenges: performance, prompt inconsistency, and output unpredictability.
• Helps convert LLM text into machine-readable formats (e.g., JSON).
• Enables function calling, where the LLM selects actions from defined tools.
• Supports building “agents” (systems that do things, not just say things).
• Turn text into vectors to measure semantic similarity.