From Prompt Engineering to Context Engineering: What Changed and Why It Matters

Context engineering is emerging as the successor discipline to prompt engineering for production LLM systems. While prompt engineering focuses on crafting a single text input, context engineering covers the full information environment a model receives: system instructions, retrieved documents, tool results, and conversation history. The post explains why prompt engineering breaks down in agentic, multi-step workflows and introduces four core strategies for managing context: write (persist for later), select (retrieve what's relevant), compress (fit within token limits), and isolate (separate concerns across agents). Real-world patterns like RAG pipelines, tool use, and memory management are discussed, along with four context failure modes: poisoning, distraction, confusion, and clash. Practical guidance is provided for shifting from prompt tweaking to designing context pipelines with proper observability and token budgeting.