Discover how Meilisearch uses LogRocket’s MCP and Galileo to improve free trial conversion and adoption of newly shipped features across its Cloud product.
Resolve cross-functional conflict in AI product development with a 3-step framework to clarify risks, assign owners, and align teams to act.
Learn when prompt-first UX adds friction and how product managers can choose AI interfaces that balance flexibility, structure, and control.
Is the foldable iPhone true product innovation? See how PMs can assess user needs, weigh costs, and separate real value from technical hype.
Explore agentic AI security trade-offs product managers must own before launch, from permissions and human oversight to memory and recovery.
See how to define AI features that deliver user value. Validate needs, assess costs, and set reliability requirements before the team builds.
Learn how to read a P&L statement, assess product costs, build stronger business cases and align product roadmaps with financial priorities.
Learn how product managers can design products for both humans and AI agents with reliable APIs, permissions, errors, and workflows.
See how a cloud storage PM uses LogRocket’s MCP to power Otto, an internal tool that flags bugs and tracks feature adoption automatically.
Learn how much AI fluency product managers need, which skills matter most, and why human judgment remains their competitive advantage.
Add a harm score to product prioritization to catch severe user consequences that RICE and other frameworks can miss.
Learn how product managers can uncover invisible features that reduce friction, protect user trust, and create a stronger competitive edge.
