Prompt engineering had its moment as the skill everyone needed to learn. It matters - but for real business AI, it is the smaller half of the story. The larger half is context engineering, and confusing the two leads people to polish their wording when the real problem is what the model can see.
Prompt engineering: the instruction
A prompt is what you ask the model to do: the task, the format, the tone, the constraints. Prompt engineering is crafting that instruction well - clear, specific, with examples where they help. It genuinely improves output, and it is worth doing.
Context engineering: the information
Context is what the model can see when it answers: your documents, records, definitions, and current facts. Context engineering is assembling exactly the right information for each task. It determines whether the model is reasoning about your actual reality or guessing from general training.
Why context usually wins
You can write a flawless prompt, but if the model cannot see your data, it will still give a generic or invented answer. Most "the AI keeps getting it wrong" problems are not wording problems - they are context problems. The best prompt in the world cannot compensate for information the model was never given.
Where each matters
Prompt engineering shapes how a capable, well-informed model responds. Context engineering makes sure it is well-informed in the first place. You need both, but if output is wrong or generic, look at context first - it is usually the culprit.
Tell us what your AI keeps getting wrong and we will find whether it is a prompt problem or, more likely, a context one.