BairesDev’s Justice Erolin reports that across client accounts in industries including autonomous vehicles, logistics, e-commerce, enterprise SaaS, and collectibles, no standout result came from a better model; every one came from getting better context to the model. A principal engineer stated that model size and quality stopped being the constraint, and the constraint now is the context the AI has and the tools it can use to get it. Erolin applies the Johari Window, a model built in 1955 by two psychologists, and the structure made famous by Donald Rumsfeld with “known knowns” and “unknown unknowns,” to AI. The framework uses two axes: what you know, and what the AI receives. Quadrant 1, “You Know it, You Supply it,” is prompt engineering, which works but is expensive because the cost is your best people hand-feeding the same architecture and business rules to a model that forgot it overnight. One engineer on a logistics account hit a wall when his agent’s context window kept filling up mid-task, and resuming yesterday’s work meant rebuilding yesterday’s context by hand.