How humans and AI work together
Context engineering is the practice of giving an AI system the right information, instructions, tools, memory, examples, and constraints at the right moment so it can produce a useful result. It goes beyond writing a clever prompt by designing the complete information environment in which the model or agent operates.
In one sentence
Better AI work usually comes from better context, not simply a better command.
Most companies can access comparable foundation models. Their differentiation comes from what those models are allowed to understand: the company’s customers, positioning, products, evidence, past decisions, brand standards, performance signals, workflows, and definition of quality.
Context is also finite. Giving a model everything can be as damaging as giving it too little. Effective context engineering selects, structures, refreshes, and prioritizes what matters for the task.
“Write a launch email” is a prompt. A context-engineered system also knows the target segment, product promise, competitive alternatives, objections from recent calls, approved proof points, brand voice, claims it may not make, channel history, and conversion goal. It retrieves only the relevant material, shows the model examples of strong prior work, and evaluates the draft against a launch rubric.
Context engineering is not stuffing a large context window with documents. Uncurated information can introduce contradictions, stale facts, irrelevant detail, and hidden instructions. Context must be governed like a product: sourced, permissioned, current, structured, and tested.
Prompt engineering taught us to ask better. Context engineering asks whether the system understands enough to help. For GTM, that context is not a folder called “brand.” It is a living model of the market, buyer, company, strategy, evidence, and work in progress.
Established concept
Written by Scott Salkin, a founder, operator, and former B2B software CMO. Published August 4, 2026. Last reviewed August 4, 2026.
