In the past two years, 'Prompt Engineering' has been an essential introductory course for almost all AI application developers. But as the large model enters the production environment, more and more teams realize that what truly determines the application effect is often not the carefully polished prompt, but what the model "sees" every time it is called. This is precisely why 'Context Engineering' has been repeatedly discussed.
The so-called context engineering refers to the systematic design and organization of all information fed into the model context, including system instructions, retrieved documents, historical conversations, tool call results, and current task status. The prompt words are just a small part of it. A mature AI application needs to dynamically decide which information to retain, which to compress, and which to discard within a limited contextual window.
Why is it more important than prompt words? Because in real business, models are never faced with an isolated question, but a constantly changing task environment. Users may constantly adjust their requirements in multiple rounds of conversations, and agents may need to switch back and forth between multiple tools. If the context is well-organized, even the most beautiful prompt words cannot save the result.
At present, there is a growing consensus in the industry that long context should be managed as a "budget" rather than being endlessly piled up; The second is to inject truly relevant information at the appropriate time through methods such as retrieval, summarization, and structured memory; The third is to let the agent maintain a working memory on their own, recording completed and pending tasks.
For developers, this means that the focus of skills is shifting from 'being able to write prompt words' to' being able to design information flow '. This may be the true watershed for AI applications to move towards engineering.
【 Reference Source 】 Comprehensive compilation of industry information released publicly