Juejin Author Yi Lu 82's Breeze: How Context Engineering Breaks AI Coding Hallucination Bottlenecks in Large Codebases

As AI coding tools become widespread, many teams find that large language models perform well in single-file demos but frequently suffer from "hallucinated modifications" or "cross-file dependency omissions" in enterprise-level Monorepos with hundreds of thousands of lines. Juejin author "Yi Lu 82's Breeze" (一缕82年的清风) argues that this is not due to insufficient model intelligence, but rather the limitations of Prompt Engineering. He contends that what truly determines the upper limit of AI programming is Context Engineering—the ability to feed the model the highest density of non-conflicting code and architectural facts within a limited token budget.

The author analyzes three structural reasons for the failure of prompt engineering: first, casually pasting files mixed with logs and temporary code causes a sharp drop in effective context token density, leading to attention drift; second, long text windows do not equate to high recall rates, as the "Lost in the Middle" phenomenon means feeding the entire codebase can actually cause logical divergence; third, if the team's implicit architectural contracts (such as DTO paradigms and domain boundaries) are not converted into machine-understandable slices, even the most elaborate prompts are baseless.

To address this, the article proposes four governance principles for context engineering: pass only skeletons, not implementations (providing only method signatures and type definitions), enforce dependency boundary isolation (clarifying read-only contracts), and dynamic context slice injection (collecting direct dependencies based on AST). The author provides a Python script example demonstrating how to automatically extract interface and function declarations from TypeScript files using regular expressions, generating a compact API_CONTRACTS.json manifest for use in CI/CD or local pre-commit hooks.