Context Engineering Is Replacing Prompt Engineering as the Core Discipline

The emerging 2026 consensus: if the model understands your intent but lacks the right information, that's a context problem, not a prompt problem. A well-crafted prompt in a poorly engineered context still fails; a mediocre prompt in a well-engineered context often succeeds. The practical shift: treat the LLM as a CPU, the context window as RAM, and your job as the OS — loading exactly the right data for each task. LangChain's four formalized strategies: write (persist externally), select (RAG), compress (summarize), and isolate (separate agent contexts).

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Sources: Neo4j Blog · Firecrawl Blog · deepset Blog