AI Product Management for PM role

Define product strategy and distinguish it from product delivery and project management.

Explain how product vision, strategy, and tactics relate to each other in a product hierarchy.

Conduct a competitive landscape analysis for a given product domain.

Identify and articulate a product's unique value proposition using a positioning statement template.

Apply a strategy canvas to visualize and communicate a product's differentiation against competitors.

Evaluate a sample product strategy statement for clarity, coherence, and alignment with business goals.

Distinguish between generative and evaluative research and identify when to use each.

Write a problem statement that separates the user problem from proposed solutions.

Design a user interview script that avoids leading questions and surfaces genuine needs.

Synthesize qualitative user research findings into actionable opportunity themes.

Create a lightweight persona based on research data rather than assumptions.

Apply the Opportunity Solution Tree framework to map a desired outcome to potential solutions.

Define and distinguish between input metrics, output metrics, and guardrail metrics.

Identify common pitfalls in metric selection such as vanity metrics and Goodhart's Law.

Select a North Star metric for a given product and justify why it captures value creation.

Design a metric tree that connects a North Star metric to actionable team-level inputs.

Explain the structure and logic of an A/B test including hypothesis, control, variant, and significance.

Interpret a sample A/B test result and make a ship/no-ship recommendation with reasoning.

Explain the RICE scoring model and apply it to a set of candidate features.

Compare common prioritization frameworks (e.g., RICE, MoSCoW, Value vs. Effort) and their limitations.

Apply the Kano model to classify features based on user satisfaction.

Distinguish between outcome-based and feature-based roadmaps and explain when each is appropriate.

Build a now-next-later roadmap for a sample product based on prioritized opportunities.

Justify a prioritization recommendation for a set of competing features to stakeholders.

Articulate trade-offs when deprioritizing a feature requested by a key stakeholder.

Prepare a product review narrative that communicates strategy, progress, and trade-offs to senior leadership.

Describe the stages of the product lifecycle from introduction through decline and identify appropriate strategies for each.

Explain the concept of product-market fit and describe signals that indicate it has been achieved.

Design a go-to-market plan outline for a new product feature launch.

Determine when to sunset a feature or product and outline a responsible deprecation plan.

Distinguish between continuous discovery and delivery and explain how they interact in an ongoing product lifecycle.

Describe what a machine learning model does in terms of inputs, outputs, and training data.

Explain the difference between supervised, unsupervised, and reinforcement learning at a conceptual level.

Explain precision, recall, and accuracy trade-offs and their product implications.

Identify product scenarios where ML adds genuine value versus where rules-based logic suffices.

Describe the ML product development lifecycle: data collection, training, evaluation, deployment, and monitoring.

Explain what a feedback loop is in an ML-powered product and why it is critical for success.

Explain at a high level how large language models generate text, including the role of prompts and tokens.

Describe key LLM capabilities and limitations, including hallucination, context windows, and latency.

Distinguish between fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) as product design choices.

Evaluate a proposed GenAI feature for feasibility by mapping it to known LLM strengths and weaknesses.

Identify responsible AI considerations specific to GenAI products: bias, safety, transparency, and user trust.

Describe how to define quality and success metrics for a GenAI-powered feature.

Define what an AI agent is and distinguish it from a simple LLM prompt-response interaction.

Explain core agent components: planning, memory, tool use, and action execution.

Describe the ReAct (Reason+Act) agent architecture at a conceptual level.

Describe multi-agent systems at a conceptual level, distinguishing between cooperative and competitive interactions.

Identify product use cases well-suited to an agentic approach versus a single-call LLM approach.

Evaluate the trade-offs of agent autonomy versus human-in-the-loop control for a given product scenario.

Describe how to scope an MVP for an agent-powered product feature.

Author the key sections of a Product Requirements Document (PRD) for an AI feature, including problem statement, success metrics, data requirements, and model performance expectations.

Identify common sources of AI uncertainty and error and their potential impact on user trust.

Describe and evaluate UX patterns for communicating AI uncertainty and providing recourse for errors.

Compare automated metrics and human evaluation approaches for assessing AI product quality.

Design a phased rollout plan for an AI feature, justifying the choice of strategy (e.g., canary, beta) to mitigate risks.

Identify signals of model performance degradation and determine criteria for triggering a model retrain.

Evaluate when to expand scope versus maintain current functionality for a deployed AI feature based on user feedback and business value.

Define the conditions under which an AI product strategy should be pivoted versus persevered, and articulate the strategic rationale.

Given a business problem, decompose it to frame a specific user problem suitable for an AI solution.

Propose a high-level AI-powered product concept and articulate its strategy and value proposition.

Define potential features for the concept and create a prioritized MVP recommendation using a suitable framework.

Define a North Star metric and outline a high-level go-to-market plan for the proposed MVP.