LLMs for product managers
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
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How AI Is Redesigning Product Management and Knowledge Work
A practical operating model for delegating product work to AI while protecting judgment, evidence quality, accountability, and customer outcomes.
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From AI Capability to Measurable Workplace Productivity
A practical operating model for choosing AI work, defining acceptable output, controlling review costs, and proving productivity in the workflow.
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How Neural Networks Represent Information—and Why It Matters
A practical model of weights, activations, neurons, features, and circuits—and how to judge claims about what an AI system has learned before trusting them.
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How to Evaluate AI Models Beyond Headline Benchmarks
A practical framework for testing AI models on real workflows, failure modes, consistency, human effort, and cost before making a production choice.
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How to Build a Durable Career in AI Product Management
A practical guide to choosing an AI product lane, building credible evidence in your current role, and judging job offers beyond titles and hype.
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How Interactive Frontier Models Change AI Product Strategy
A practical framework for choosing, designing, evaluating, and safely shipping AI responses that become charts, forms, calculators, and actions.
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A Product Leader’s Playbook for Every New AI Model Release
A practical system for deciding when a new AI model deserves testing, how to evaluate it, and when to adopt, watch, or walk away.
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Five Transferable AI Habits to Build with an Approved Copilot
Use an approved Copilot to master task framing, grounding, workflow design, evaluation, and learning habits that survive the next model switch.
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Claude 5.5 Task Economics: Price Outcomes, Not Tokens
A practical framework for testing Claude 5.5 on real work and measuring model spend, correction time, retention, and cost per accepted outcome.
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A Practical System for Personal Multi-Model AI Workflows
Build a portable AI workflow that routes work by job, tests models fairly, and turns repeated corrections into reusable skills without vendor lock-in.
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Andrew Ng on AI, Learning, and Redesigning Work
A practical playbook for redesigning AI-assisted work, protecting durable learning, hiring for judgment, and governing real risks without panic.
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A Practical AI Evaluation Workflow for Product Teams
Build a repeatable AI eval workflow that turns vague quality debates into traceable failure rates, safer experiments, and clearer release decisions.
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How to Critically Evaluate AI Answers Before You Act
A practical framework for testing AI claims, exposing assumptions, matching scrutiny to risk, and keeping human judgment in consequential decisions.
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How to Evaluate and Optimize Open Models for Production
A practical framework for deciding whether an open model is production-ready, then improving quality, latency, cost, and reliability.
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How Product Leaders Should Read Frontier AI Benchmarks
A practical framework for separating benchmark gains from deployable capability and turning frontier model progress into product decisions.
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How to Benchmark AI Models for Cost, Quality, and Risk
A practical framework for choosing AI models by cost per accepted result, workflow reliability, failure severity, and production economics.
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Chinese Frontier AI Models: A Product Leader’s Decision Guide
A practical framework for choosing Kimi K3, Qwen 3.8 Max, or DeepSeek V4 Flash by completion reliability, accepted-result cost, and product fit.
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A Practical Guide to Metaphors for Understanding Language Models
A practical framework for choosing AI metaphors, exposing their hidden assumptions, and translating human-sounding behavior into testable product claims.
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Claude Code for Non-Technical Professionals: A Safe Start
A practical, low-risk way to use Claude Code for file-based product work, with a bounded first project, review gates, and reusable prompts.
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How I Make AI Agents Speak Like Our Team: A Conversation Design Playbook That Lifts CSAT
Your AI Agent will sound like an LLM unless you intentionally design how it communicates. I share a practical conversation design playbook covering tone, response structure,…
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Browse topics
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