Option C accurately captures Anthropic’s distinction. Prompt engineering primarily concerns how instructions are written, structured, and organized to obtain the desired behavior from a particular model invocation. Techniques include explicit instructions, examples, roles, XML structure, output requirements, and task-specific prompt construction. Context engineering operates at a broader architectural level: it determines which information should actually be present in the model’s context at each inference step.
Anthropic defines prompt engineering as methods for writing and organizing LLM instructions, whereas context engineering encompasses strategies for curating and maintaining the optimal set of tokens during inference. For long-running agents, context can contain system instructions, tools, MCP resources, retrieved documents, prior messages, tool results, summaries, and memory.
This distinction matters because multi-step agents continuously generate new state. Effective systems may prune obsolete results, retrieve information just in time, compact earlier conversation history, isolate subagent contexts, or store persistent state externally. B is incorrect because context engineering has not simply replaced prompt engineering; the two operate at different scopes. A defines context too narrowly, and D obscures an important architectural distinction.
Therefore, C correctly represents Claude Developer coverage of prompt engineering versus context engineering, context curation, agent state, long-horizon workflows, and context-window optimization.
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