Context engineering is the discipline of designing the full information environment an LLM receives - not just the prompt, but retrieved knowledge, tool results, and conversation history. Here's what changed and why it matters for production AI systems.

BigData Boutique blog

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.

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

The Rise and Limits of Prompt Engineering

Prompt Engineering vs Context Engineering: A Side-by-Side Comparison

Getting Started: From Prompt Tweaker to Context Architect