RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is a technique that combines information retrieval with AI text generation. Instead of relying solely on what a model was trained on, RAG retrieves relevant documents at query time and uses them to ground the response. This reduces hallucination and keeps AI outputs accurate and current.
Granola: the AI notepad that keeps you the author of your meetings
Granola is an AI notepad for meetings that merges your own rough notes with a full transcript. No bots in the call, and you stay the author while AI does the tidying up.
Lennybot: A curated second brain for product leaders
Lennybot is a worked example of expert-clone AI. Hundreds of named practitioners curated into a queryable second brain, with citations, voice mode, and a freemium entry point. Worth knowing as a tool and as a product pattern.
What is a Second Brain (and Personal Context Management)?
A Second Brain is an external system that remembers for you. In 2026, Tiago Forte renamed the discipline Personal Context Management — because the bottleneck has moved from capture to context, and AI quality is now a context-choice problem.
What is AI memory?
AI memory is the emerging set of techniques that let AI systems remember who you are, what you've told them, and how you work, across conversations, sessions, and tools. Most organizations are only using two of its five layers.
What Are MCP Apps?
In Issue #12, I introduced term MCP - the open standard that acts as a "USB-C port for AI," letting AI models connect to external tools and data sources through a universal interface. Since then, MCP has grown rapidly.
AI Strategic Inflection Points: Transforming Business Models
Drawing on insights from MIT Sloan's Artificial Intelligence: Implications for Business Strategy program and Gartner research, this framework outlines key principles for successfully leading AI-driven transformation.
Invent and Simplify: Lessons from an AI-First Leader
Sometimes the best insights come from people who've lived through what the rest of us are only reading about. This issue features a guest article.
AI Readiness Assessment: Where Does Your Organization Stand?
When I work with executives, leadership teams, and AI tasks forces on their AI journey and stratgey, sooner or later a key question comes up: should they build AI solutions in-house, purchase them from vendors, or ado...
TempoHack: AI-Powered Time Tracking I Built Myself
I watch a lot of podcasts and video content. It's one of my primary sources for ideas, frameworks, and insights that make their way into this newsletter and my client work.
What Is Interpretability vs. Utility?
This Week's Term: Interpretability vs.
From AI Pilots to Production: The MIT Maturity Model
Many organizations have run an AI pilot by now. The interesting question is: what happened next?
Success and Scale Bring Broad Responsibility
I was a few years into my time at Amazon Web Services when this principle was introduced in 2021.
Miro AI: Putting Intelligence on the Shared Canvas
Most AI tools solve for individual productivity. You ask ChatGPT a question, get an answer, and bring that answer back to your team.
Building the Business Case for AI Investments
When I review AI initiatives with business leaders, I see a concerning pattern. Teams are excited, budgets are allocated, tools are being evaluated. But when we ask "How exactly will this create value for your business?
Insist on the Highest Standards in the Age of AI
Insist on the Highest Standards - your role as a leader in the age of AI.
The Five Levels of AI Autonomy
Everyone's racing to build "AI agents," but most companies are thinking about this wrong.
What Is Retrieval-Augmented Generation (RAG)?
This Week's Term: Retrieval-Augmented Generation (RAG) - an AI architecture that combines large language models with dynamic information retrieval, allowing models to fetch relevant documents or data before generating...