The difference between AI applications that feel magical and those that frustrate users often comes down to context engineering decisions made months before launch. In this chapter, we'll dissect the context architectures behind some of the most successful AI products in the world—ChatGPT, Cursor IDE, Perplexity, and enterprise RAG systems at Fortune 500 companies.
123456789101112interface CodeContext { currentFile: FileContent; cursorPosition: Position; selection?: TextRange; relatedFiles: FileContent[]; typeDefinitions: TypeDef[]; recentEdits: Edit[]; } async function assembleCodeContext( request: CompletionRequest, codebase: CodebaseIndex
123456789101112interface CursorContext { immediate: { currentFile: string; cursorPosition: Position; selectedText?: string; visibleRange: Range; }; semantic: { relatedFunctions: CodeSymbol[]; typeDefinitions: TypeDef[]; importedModules: Module[]; referencedBy: Reference[];
123456789101112interface SearchResult { url: string; title: string; snippet: string; relevanceScore: number; publishDate: Date; domain: string; } interface AttributedClaim { claim: string; sourceIndices: number[];
123456789101112from dataclasses import dataclass from typing import List, Dict import numpy as np @dataclass class ContextQualityMetrics: relevance_score: float # 0-1, semantic similarity to query coverage_score: float # 0-1, % of query aspects addressed freshness_score: float # 0-1, recency of information diversity_score: float # 0-1, variety of sources/perspectives coherence_score: float # 0-1, logical flow and consistency efficiency_score: float # 0-1, information density vs tokens used