As AI integration becomes standard in modern applications, developers face a very critical decision: How do we effectively communicate with large language models (LLMs) to get reliable and accurate results? The answer lies in understanding two distinct but often confused approaches: prompt engineering and context engineering.
While these terms are frequently used interchangeably, they represent fundamentally different strategies for working with AI systems. Understanding their differences is important for building robust and production-ready applications that leverage LLMs effectively.