Читать книгу Habit Machine. AI Product Management - Ар'лан ис'Дрекхэм - Страница 45

AI isn’t a feature toggle. It’s infrastructure. Operators who treat it as a novelty fall behind. Operators who treat it as a lever reshape product cycles. Four capabilities now separate baseline PMs from modern ones.

1. AI Prototyping & Conversational UX

Screens are no longer the primary interface. Conversation, prediction, and ambient triggers are. AI prototyping means designing system behavior under uncertainty: prompt flows, fallback logic, confidence thresholds, and human-in-the-loop handoffs. You’re not just mapping states. You’re mapping reliability.

— Test conversational workflows before engineering buildout using AI mockups or synthetic user panels.

— Define quality criteria upfront: accuracy, latency, tone, and escalation paths.

— Validate interaction preference: chat, form, voice, or hybrid. Users reveal intent through usage, not surveys.

2. RAG Architecture & Trust Calibration

Retrieval-Augmented Generation (RAG) is the backbone of most trustworthy AI products. It grounds LLM outputs in verified sources: internal docs, customer records, policy libraries, or product catalogs. A PM doesn’t need to build the vector database. They need to understand how retrieval affects hallucination risk, latency, cost, and explainability.

— Map data freshness requirements. Stale retrieval breaks trust faster than slow retrieval.

— Design confidence indicators. Users tolerate uncertainty when the system signals it clearly.

— Audit fallback paths. When retrieval fails, does the product degrade gracefully or collapse?

3. Vibe Coding & Rapid Validation

“Vibe coding” isn’t a gimmick. It’s the practical use of AI coding assistants and low-code stacks to compress idea-to-test cycles from weeks to hours. You don’t need to ship production code. You need to ship testable behavior.

— Build rough, functional prototypes to validate demand before over-investing.

— Collaborate directly with AI-assisted tools to explore interaction patterns without waiting for engineering bandwidth.

— Treat every prototype as a hypothesis. Measure activation, not aesthetics.

4. Agent Orchestration & Workflow Design

Modern AI products rarely run on a single model. They run on orchestrated systems where specialized agents handle research, summarization, planning, execution, and validation. The PM’s job is choreography, not model training.

— Define handoff rules. When does one agent yield to another? Where does human review stay mandatory?

— Prevent cascading errors. Gate critical outputs, parallelize independent tasks, and audit quality continuously.

— Measure system performance, not just feature completion. Latency, accuracy drift, and cost-per-task dictate scalability.