Don't treat evals as a mere checklist. Instead, use them as a creative tool to discover opportunities. A well-designed eval can reveal that a product is underperforming for a specific user segment, pointing directly to areas for high-impact improvement that a simple "vibe check" would miss.

Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.

The most valuable consumer insights are not in analytics dashboards, but in the raw, qualitative feedback within social media comments. Winning brands invest in teams whose sole job is to read and interpret this chatter, providing a competitive advantage that quantitative data alone cannot deliver.

AI product quality is highly dependent on infrastructure reliability, which is less stable than traditional cloud services. Jared Palmer's team at Vercel monitored key metrics like 'error-free sessions' in near real-time. This intense, data-driven approach is crucial for building a reliable agentic product, as inference providers frequently drop requests.

Treat product data as a reflection of human behavior. At DoorDash, realizing the order status page had 3x more views than the homepage revealed intense user anxiety ("hanger"). This insight, derived from a data outlier, directly led to the creation of live order tracking.

Top product builders are driven by a constant dissatisfaction with the status quo. This mindset, described by Google's VP of Product Robbie Stein, isn't negative but is a relentless force that pushes them to question everything and continuously make products better for users.

Unlike traditional software where PMF is a stable milestone, in the rapidly evolving AI space, it's a "treadmill." Customer expectations and technological capabilities shift weekly, forcing even nine-figure revenue companies to constantly re-validate and recapture their market fit to survive.

Developers often test AI systems with well-formed, correctly spelled questions. However, real users submit vague, typo-ridden, and ambiguous prompts. Directly analyzing these raw logs is the most crucial first step to understanding how your product fails in the real world and where to focus quality improvements.

Before diving into SQL, analysts can use enterprise AI search (like Notion AI) to query internal documents, PRDs, and Slack messages. This rapidly generates context and hypotheses about metric changes, replacing hours of manual digging and leading to better, faster analysis.