Building AI Products
What does building AI products mean?
Building AI products refers to the process of creating robust, production-quality products or features powered by AI (particularly large language models), which requires a distinct skill set beyond just using AI tools personally. This involves production-level prompt engineering, orchestrating complex workflows, designing systems for observability, implementing evals to ensure quality, and managing ongoing maintenance—all requiring close cross-functional collaboration between product managers, designers, and engineers who understand both customer needs and technical AI constraints.
What skills do teams need for building AI products?
Building AI products requires several specialized skills beyond typical product development:
Production-level prompting — Writing prompts that work reliably for thousands of users, not just a single use case. Unlike personal AI use where you can iterate until you get a good response, production prompts must work consistently across diverse inputs.
Orchestration — Breaking down complex tasks into multiple LLM calls that work together. Most AI products can't be solved with a single prompt—they require sequences of API calls, data transformations, and conditional logic.
Observability — Implementing trace logging to capture what's happening in each AI interaction, enabling teams to diagnose issues and understand system behavior.
Evals — Creating systematic methods to measure whether the AI product performs well and continues to meet quality standards over time.
These technical skills must be combined with traditional product skills—understanding customer problems, designing user experiences, and making strategic decisions about when AI is the right solution.
How is building AI products different from using AI tools?
Using AI tools personally means you can prompt ChatGPT until you get a useful response. Building AI products means creating systems that work for thousands of users without manual intervention. It's the difference between being an "AI-powered product manager" (using AI to do your job better) and an "AI product manager" (building products based on AI).
Additionally, AI products require continuous maintenance. Unlike traditional software where passing tests means lasting confidence, AI products need ongoing monitoring, customer feedback loops, and regular re-evaluation to ensure quality doesn't degrade. Teams must factor in this "forever maintenance cost" when deciding to build with AI.
Learn more:
- AI Changes Everything (And Nothing At All)
- How I Designed & Implemented Evals for Product Talk's Interview Coach
- Building AI Products - All Things Product Podcast with Teresa Torres & Petra Wille
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