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February 26, 2026•15 min read

From Chatbot to Agent: The PM's Guide to Agentic AI Products

Chatbots respond. Agents act. The shift from conversational AI to agentic AI is the biggest product paradigm change since mobile, and most PMs aren't ready for it.

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March 8, 2026•14 min read

How LLMs Actually Work: A Product Manager's Mental Model

You don't need to read the 'Attention Is All You Need' paper, but you do need a working mental model of what's happening under the hood. Here's the PM-friendly version.

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March 6, 2026•13 min read

Context Windows, Memory, and Why AI Forgets: Designing Around Token Limits

Every AI conversation has an invisible expiration date. Understanding context windows is the difference between an AI feature that delights and one that frustrates.

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March 4, 2026•14 min read

The Cost of Intelligence: Understanding LLM Pricing, Tokens, and Latency Tradeoffs

Your AI prototype works beautifully at 100 users. At 100,000 users, it costs more than your entire engineering team. Here's how to think about LLM economics.

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March 2, 2026•15 min read

Grounding AI in Reality: RAG, Guardrails, and Stopping Hallucinations

Hallucinations aren't bugs—they're the default behavior of language models. The question isn't whether your AI will make things up, but what systems you build to catch it when it does.

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February 28, 2026•14 min read

Evaluating AI Features: How Do You Know If Your LLM Is Actually Good?

You can't unit test a language model. The shift from deterministic to probabilistic software demands entirely new approaches to quality, and most teams are winging it.

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February 24, 2026•14 min read

Designing Human-in-the-Loop AI: UX Patterns for Uncertainty

An AI that's 95% accurate still fails 1 in 20 times. The difference between a trusted product and a dangerous one isn't the model—it's the UX that wraps it.

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February 22, 2026•14 min read

The AI Build vs. Buy Decision: When to Use Foundation Models, Fine-Tune, or Build In-House

Every PM is now an AI PM. The hardest question isn't 'should we use AI?' — it's 'which layer of the stack should we own?' Here's the decision framework.

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February 20, 2026•13 min read

AI Product Metrics That Actually Matter: Beyond 'We Added AI'

Your AI feature has 10,000 daily users. But are they using it because it's good — or because it's in their way? Here's how to measure what actually matters.

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From Chatbot to Agent: The PM's Guide to Agentic AI Products

More Articles

How LLMs Actually Work: A Product Manager's Mental Model

Context Windows, Memory, and Why AI Forgets: Designing Around Token Limits

The Cost of Intelligence: Understanding LLM Pricing, Tokens, and Latency Tradeoffs

Grounding AI in Reality: RAG, Guardrails, and Stopping Hallucinations

Evaluating AI Features: How Do You Know If Your LLM Is Actually Good?

Designing Human-in-the-Loop AI: UX Patterns for Uncertainty

The AI Build vs. Buy Decision: When to Use Foundation Models, Fine-Tune, or Build In-House

AI Product Metrics That Actually Matter: Beyond 'We Added AI'

Let's work together.