Imagine hiring the smartest person you can find, on their first morning. You ask them to handle a customer complaint. They would struggle, not because they lack ability, but because they know nothing about your customer or your product.
An AI model is in the same position on every single request. It knows a great deal about the world in general. It knows nothing about your situation unless you tell it.
Context and the Context Window
What you tell the model is called the context. And context has a limit. Everything the model can consider at once has to fit in a fixed space called the context window. Your instructions, the documents and the conversation so far all share that same space.
So more is not better. Fill the window with irrelevant material and the useful part gets buried. The skill is choosing what goes in.
An Example
Ask a model: should we refund this customer? With no context, it gives a generic answer. Add the order history and the refund policy, and the same model gives the right one.
The work of choosing and arranging that information has a name: context engineering. It means getting the right information to the model at the right moment.
Why Your Own Data Matters
Everyone can rent the same model. Nobody else has your records, your documents and your history. The advantage is in what you can put in the window.
Context, Not Capability builds on Context Window. Context has a limit: everything the model can consider must fit the window, so choosing what goes in is the work.
Context Engineering builds on Context, Not Capability. Same model, different desks, different answers: context engineering turns the context-not-capability point into four choices.
Context, Not Capability contrasts with Scaling Laws. Scaling laws explain building bigger models; this lesson says a very capable model still answers poorly without your context.
The Model and the Harness builds on Context, Not Capability. Both say the model is rarely the limit; the harness adds the instructions, tools and memory that supply context.
Context, Not Capability is an example of Complementary Inputs(lesson unlocks soon). A capable model without your records gives a generic answer: more model cannot replace the missing input.
The Business Engineer framework maps where each part of the AI stack sits and who holds the leverage. These pills are short lessons from the same library.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
