Explain how you would build reliable data instrumentation for AI writing assistant

Problem Statement Description

You are working on an AI writing assistant used by first-time buyers who may be unfamiliar with question design, editing workflows, or how to judge the quality of AI-generated content. The product team wants to understand whether the assistant is producing useful, trustworthy, and high-quality writing outputs across common workflows such as drafting, rewriting, summarizing, tone adjustment, and grammar improvement.

Your task is to explain how you would build reliable data instrumentation for this product. The focus is not on designing the writing assistant itself, but on defining what events, data flows, quality signals, logging standards, and observability systems are needed so teams can measure product performance, user outcomes, and model behavior with confidence.

Assume the product operates at scale, handles sensitive user-written content, and may involve multiple surfaces such as web, mobile, browser extensions, and document editors. The instrumentation should help product, engineering, data science, and responsible AI teams make decisions without compromising user privacy, security, or trust.

The experience should consider:

- Key user workflows to instrument, from first session and question entry through generation, editing, acceptance, export, or abandonment

- Event taxonomy, schemas, identifiers, timestamps, and data quality standards needed for consistent measurement

- Signals that indicate output quality, user satisfaction, friction, trust, and repeated value for first-time buyers

- API, model, and system-level telemetry such as latency, errors, retries, moderation flags, and response completion

- Privacy and security constraints around collecting, storing, or analyzing user-generated text and AI outputs

- Reliability mechanisms for missing, duplicated, delayed, or inconsistent events across platforms

- Observability, alerting, dashboards, and auditability for product, engineering, and model-quality stakeholders

- Rollout, validation, experimentation, and governance considerations before using the data for decisions

The goal is to show how you would approach instrumentation as a Technical PM: clarifying requirements, defining reliable data contracts, balancing product insight with responsible AI constraints, and enabling teams to evaluate whether the AI writing assistant is delivering high-quality outcomes for new users.