context engineering — the evolution of prompt engineering
core idea:
prompt engineering = how to write a good prompt
context engineering = how to assemble the entire context for LLM at each step (system prompt + tools + history + external data + MCP)
why it matters:
• LLMs have an "attention budget" — more tokens = worse focus
• context rot — recall quality degrades as context grows (even in top models)
• n² pairwise relationships between tokens — architectural constraint of transformers
in practice:
• system prompts: find the "right altitude" — not too rigid (brittle), not too vague
• tools: minimal set, clear descriptions, no overlapping functionality
• examples: fewer edge cases, more canonical examples
• just-in-time retrieval: don't preload everything — keep lightweight pointers (paths, links) and fetch on demand
tl;dr: context is a finite resource with diminishing returns. goal: minimal set of high-signal tokens for the desired outcome
core skill for agentic AI. can't build agents without it 💎
1February 5, 2026 439 1
