Every breakthrough AI product you've used—from ChatGPT to GitHub Copilot to Notion AI—shares one critical commonality that has nothing to do with the underlying model. The difference between an AI feature that delights users and one that frustrates them comes down to a discipline that most teams overlook entirely: context engineering.
123456789101112interface ContextConfig { maxTokens: number; sections: { name: string; priority: number; // Lower = higher priority minTokens: number; maxTokens: number; required: boolean; }[]; } async function assembleContext(
123456789101112interface ContextBlock { type: 'system' | 'user' | 'retrieved' | 'history' | 'metadata'; content: string; priority: number; tokens: number; timestamp: Date; } class ContextAssembler { private maxTokens: number; private blocks: ContextBlock[] = [];
123456789101112import numpy as np from datetime import datetime, timedelta from typing import List, Dict def score_context_relevance( query_embedding: np.ndarray, context_items: List[Dict], recency_weight: float = 0.3, similarity_weight: float = 0.7 ) -> List[Dict]: """Score and rank context items by relevance to query."""
123456789101112{ "system_prompt": { "identity": "You are a customer support agent for [Company]. You help users with [product] questions.", "capabilities": [ "Answer questions about features and pricing", "Help troubleshoot common issues", "Guide users through account management", "Escalate complex issues to human agents" ], "constraints": [