Previously, users focused on well-crafted prompts, prompt engineering, to get the most from AI models. Prompt engineering worked well for one-off tasks, but it showed its limit when applications grew more complex or required up-to-date data or long-term memory. Context engineering solved this problem by providing AI models with additional knowledge to perform better. This context can be embedded in the prompt directly or retrieved from an external source. Both techniques can be used separately or together. Context engineering complements prompt engineering rather than replaces it. In essence, prompts guide the AI model while context provides the knowledge. CIOs and AI leaders, who misapply prompt or context engineering, risk creating AI systems with inconsistent outputs, leading to wasted AI investment and loss of trust. Understanding when to use each approach is critical to building accurate and stable AI systems.