Context engineering is the newer, broader term for the practice that includes prompt engineering, knowledge base setup, system prompt design, and tool integration. Here is why the term matters and what it covers.
Context engineering is the discipline of structuring all the inputs an AI model sees to produce reliable, useful outputs. This includes: the prompt itself, the system prompt, the knowledge base loaded, the tools the model has access to, the conversation history.
Prompt engineering is one component. Context engineering is the whole picture.
In early AI applications (2022-2023), the prompt was the dominant variable. Better prompts produced better outputs.
In modern AI applications (2025+), the prompt is one of many variables. The system prompt, knowledge base, retrieved documents, conversation history, and tool definitions all shape the output.
Context engineering captures this broader reality. The skill is no longer just "write better prompts" — it is "structure the entire context the model sees."
The skill required is broader than "prompting". Building real AI workflows means designing the entire context, not just writing good prompts.
Hiring shifts. "Prompt engineer" is becoming "AI engineer" or "AI workflow designer."
Tool selection matters. Tools like Claude Projects make context engineering easier by managing knowledge bases, system prompts, and conversation history in one place.