The discipline of designing, curating, and managing the information provided to a large language model so it produces the best possible output for a specific task.
Prompt engineering asks: how do I phrase this request? Context engineering asks a harder question: what does the model need to know, and what should it not see?
The term was formalized in late 2025 as AI systems moved from single-turn chat interactions to multi-step agent workflows. In a chat, you can rephrase your prompt if the output misses. In an agent workflow running 15 steps autonomously, the model cannot be re-prompted at every step. The information environment it operates in determines whether it succeeds or fails.
What context engineering involves
The work includes selecting which documents, data, and instructions go into the model’s context window, compressing or summarizing information that would otherwise consume too many tokens, isolating context between sub-tasks so noise from one step does not bleed into another, and structuring reference materials so the model can find what it needs without wading through what it does not.
What most people get wrong
Teams pour effort into writing the perfect prompt and ignore everything around it. A mediocre prompt inside a well-curated context window will outperform a brilliant prompt buried in noise. The model’s attention is finite. Filling the context window with irrelevant history, redundant instructions, or stale data degrades output quality regardless of how sharp the prompt is.
Why it matters now
As AI agents handle more complex tasks inside marketing stacks, the quality of the context they receive becomes the primary lever for output quality. Context engineering is the emerging discipline that separates teams getting inconsistent results from teams getting reliable ones.