What it means in practice
If AI is not giving you the result you want, the fault is almost always in one of two things: the prompt, or the context. Context engineering deals with the second.
The strongest question you can ask inside a company is: how do we give AI enough context to do this on its own from now on? The answer is usually surprisingly concrete:
- access to the data and to customer email
- the knowledge base and internal policies
- specifications and product briefs
- samples of what a good result looks like
- a brief on the tone of communication
- log files and user feedback
- product photography
An example from practice
At one trading client, marketing communication was inconsistent and did not sound like the founders. The fix was not better prompts. We took podcast conversations with the CEO, COO and founders, transcribed them, and put them into a custom GPT’s knowledge base alongside the company handbook and internal memos.
The output changed overnight, not because the model got better, but because it finally knew what it was talking about.
Why data access is the biggest multiplier
An agent without data can guess. An agent with access to the inbox, the database and the documents can work. The reverse holds too: more data does not automatically mean a better result. What matters is which data, in what structure, and who should have access to it.