From Prompts to Context Engineering: AI's New Focus
Context Engineering: The New AI Focus Beyond Prompts - Learn Dynamic Systems for Better LLM Performance with RAG, Agents & Tools
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How hot is "Context Engineering" recently? Andrej Karpathy has endorsed it, and Phil Schmid's article introducing context engineering became the top story on Hacker News.
Today, we'll introduce the concept and practical applications.
In the AI era, you may have heard terms like prompt engineering, RAG, and memory. But few people mention context engineering.
Actually, this term isn't new - many agent builders have been focusing on this for the past two years. As for its importance, the diagram below well summarizes the relationship between context engineering and prompt engineering, RAG, etc.
In traditional prompt engineering, developers typically focus on carefully designing prompts to get better answers. However, as applications become increasingly complex, it's becoming clear that relying solely on prompts can no longer meet the needs of modern agents. Today, providing complete and structured contextual information is more important than any clever prompt.
Context engineering was born for this purpose.
Context engineering is building dynamic systems that provide the right information and tools in the right format, enabling LLMs to reasonably complete tasks.
Most of the time, when an agent doesn't perform tasks well, the root cause is not conveying appropriate context, instructions, and tools to the model. LLM applications are evolving from single prompts to more complex, dynamic, intelligent systems.
Therefore, context engineering is becoming the most important skill that AI engineers can develop.