--- title: "A Deep Look at the Limits of Vibe Coding" description: "An in-depth look at seven limits of Vibe Coding, with concrete examples and practical insights to help developers better understand the current state and challenges of AI-assisted coding." canonical_url: "https://blog.markkulab.net/en/post/vibe-coding-limitations" author: "Mark Ku" author_url: "https://blog.markkulab.net/en/author/mark-ku" site: "Mark Ku's Tech Notes" date_published: "2025-08-06 06:01:00 +0800" category: "AI" tags: ["ai", "vibe coding", "cursor", "copilot", "software engineering", "productivity"] language: "en" license: "CC BY 4.0" license_url: "https://creativecommons.org/licenses/by/4.0/" attribution: "when reusing or quoting, credit the author and link back to the original" --- # A Deep Look at the Limits of Vibe Coding ## The rise and challenges of Vibe Coding As AI coding tools become widespread, many developers are trying "Vibe coding" — giving the AI a brief description or direction and letting it auto-generate code. While this can produce new code quickly, real-world project development reveals plenty of limits. **The core idea of Vibe coding** is describing requirements in natural language and having AI auto-generate the matching code. This approach is genuinely useful for prototypes and quickly validating ideas, but it tends to hit various challenges in complex enterprise-grade projects. ## 1️⃣ Layering features on existing complexity is hard AI can quickly produce new modules, but problems surface when integrating new features into an already-complex system. ### Concrete example: shopping-cart discount feature **The issue:** - AI didn't account for membership-tier discount rules - Ignored special discounts by product category - Didn't handle the discount cap ## 2️⃣ For obscure or complex new problems, AI often can't answer AI's capabilities depend on its training data. When a problem is obscure or involves entirely new concepts, AI often can't provide a complete solution. ## 3️⃣ Limited "brain capacity" (Context) — hard to handle huge projects Even powerful AI has context limits — only so much code can be "remembered" at once. Beyond that, full understanding falls apart. ### Concrete example: large e-commerce site **Real problem:** - AI only sees code snippets in the current conversation - Can't understand complex inter-module dependencies - Lacks awareness of overall architecture ## 4️⃣ Insufficient "association" when implementing complex features — easy to "fix A, break B" AI still falls short on understanding internal system dependencies — it lacks the intuition human engineers build through experience. ### Concrete example: changing a price-calculation function **Consequences:** - Price calculation is correct, but sales-report data is incomplete - Statistics reports show errors - Sales data can't be tracked ## 5️⃣ Memory deficits — hard to reuse approaches it has been taught AI can learn instructions within a single conversation, but lacks long-term memory — so it can't reuse past solutions. ### Concrete example: array sorting **When asked a similar request today, AI might forget entirely and produce a less efficient version.** ## 6️⃣ Quality wobbles when switching models In many dev environments, people write part of the code with a higher-tier model (e.g., GPT-4.5), then switch to a cheaper model to save cost — and code quality drops sharply. ### Concrete example: user registration **Issues that may arise during low-tier-model maintenance: simplified versions, missing important checks** ## 7️⃣ Inconsistent style and design Unless you explicitly tell the AI which architectural principles and naming rules to follow, every output may have different style. ### Concrete example: login feature **Second time: class-based design — completely different style version** ## Many of the above can be mitigated with Cursor and Copilot guidelines ### 1. Cursor usage guide Cursor, an AI-based code editor, can dramatically boost dev efficiency — but you need the right strategy. #### Set up a project context file **Location:** project root **Filename:** `.cursorrules` **Steps:** 1. Create `.cursorrules` at the project root 2. Copy a template above and adapt to your project's needs 3. Save — Cursor automatically reads and applies these settings #### Effective Cursor prompting tips ### 2. GitHub Copilot usage guide GitHub Copilot, a code-completion tool, provides real-time suggestions — but needs the right configuration. #### Set up a Copilot config file **Location:** `.copilot` folder at the project root **Filename:** `settings.json` **Steps:** 1. Create `.copilot` at the project root 2. Inside `.copilot`, create `settings.json` 3. Copy a template above and adapt to your project's needs 4. Save — Copilot automatically reads and applies these settings #### Project file structure summary **Important reminders:** - `.cursorrules` and the `.copilot` folder should be checked into version control - These files affect AI tool behavior for everyone on the team - Recommend standardizing the format and content across the team #### Copilot best practices ### 3. Integrated usage strategy #### Build a development workflow **Example usage:** 1. Use Cursor for design: - Describe feature requirements - Provide code context - Specify architectural patterns 2. Use Copilot for implementation: - Write code in files - Use comments to guide - Accept or reject suggestions 3. Use Cursor for optimization: - Refactor code - Add tests - Ensure quality #### Common issue solutions ## Conclusion While Vibe coding can quickly generate code and accelerate prototyping, AI still can't fully replace engineers when facing large projects, complex logic, and long-term maintenance. Human engineers can grasp the entire system architecture, have long-term memory, solve unknown problems, and ensure consistent code style — none of which AI fully matches today. By using AI tools like Cursor and Copilot correctly, developers can dramatically boost efficiency. The key is establishing a sound strategy and workflow that turns AI into a capable assistant — not a substitute you completely depend on. --- ## About this article and its author Originally published on [Mark Ku's Tech Notes](https://blog.markkulab.net/en/post/vibe-coding-limitations) License: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — when reusing or quoting, credit the author and link back to the original ### About the author **[Mark Ku](https://blog.markkulab.net/en/author/mark-ku)** — Software Solution Provider - 10+ years senior software engineer, now an AI Builder - Focused on large-platform architecture — North-American e-commerce, AI SaaS subscription billing - Combining AI Agents and automation to build evolvable product foundations ### Free tools built by the author All of these are free to use: - [Free PDF Sign Tool](https://blog.markkulab.net/en/tools/pdf-sign): Online PDF sign tool — draw, type, or upload a signature, then drag, resize, and download. 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No sign-up, nothing uploaded to a server, and mermaid.live share links work as-is. - [React Intl Phone Number](https://blog.markkulab.net/en/tools/react-intl-phone-number): react-intl-phone-number is an open-source React component: framework-agnostic and antd-free, with E.164 in/out, a searchable flag / country-code dropdown, configurable validation levels (strict / mobile-strict / loose), themeable CSS, and i18n — phone logic powered by google-libphonenumber. Lightweight and fully typed. Free and open source (MIT). - [Uptime Kuma Cluster](https://blog.markkulab.net/en/tools/uptime-kuma-cluster): Turn single-node Uptime Kuma into a highly available cluster: OpenResty + Lua smart load balancing, shared MariaDB state, health checks and automatic failover, plus cluster-management REST APIs. One Docker Compose command to start. 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