Unlike traditional software where UX can be pre-assessed, AI products are inherently unpredictable. The CEO of Braintrust argues that this makes observability critical. Companies must monitor real-world user interactions to capture failures and successes, creating a data flywheel for rapid improvement.
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Unlike traditional software where problems are solved by debugging code, improving AI systems is an organic process. Getting from an 80% effective prototype to a 99% production-ready system requires a new development loop focused on collecting user feedback and signals to retrain the model.
Top product teams like those at OpenAI don't just monitor high-level KPIs. They maintain a fanatical obsession with understanding the 'why' behind every micro-trend. When a metric shifts even slightly, they dig relentlessly to uncover the underlying user behavior or market dynamic causing it.
An AI product's job is never done because user behavior evolves. As users become more comfortable with an AI system, they naturally start pushing its boundaries with more complex queries. This requires product teams to continuously go back and recalibrate the system to meet these new, unanticipated demands.
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.
People overestimate AI's 'out-of-the-box' capability. Successful AI products require extensive work on data pipelines, context tuning, and continuous model training based on output. It's not a plug-and-play solution that magically produces correct responses.
Unlike traditional software, AI products are evolving systems. The role of an AI PM shifts from defining fixed specifications to managing uncertainty, bias, and trust. The focus is on creating feedback loops for continuous improvement and establishing guardrails for model behavior post-launch.
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.
In traditional product management, data was for analysis. In AI, data *is* the product. PMs must now deeply understand data pipelines, data health, and the critical feedback loop where model outputs are used to retrain and improve the product itself, a new core competency.
Unlike traditional software, AI products have unpredictable user inputs and LLM outputs (non-determinism). They also require balancing AI autonomy (agency) with user oversight (control). These two factors fundamentally change the product development process, requiring new approaches to design and risk management.
Reviewing user interaction data is the highest ROI activity for improving an AI product. Instead of relying solely on third-party observability tools, high-performing teams build simple, custom internal applications. These tools are tailored to their specific data and workflow, removing all friction from the process of looking at and annotating traces.
