{"slug": "i-let-ai-write-my-backend-code-for-a-week-here-s-what-actually-broke", "title": "I Let AI Write My Backend Code for a Week — Here's What Actually Broke", "summary": "A developer let AI write backend code for a week and found that while AI-generated boilerplate was impressive, it introduced subtle bugs like type coercion issues, N+1 database queries, concurrency problems in token refresh, and poor error logging. The experience taught that AI is best used as a pair programmer, not a replacement for engineering judgment.", "body_md": "I told myself it would be fine. I had been using AI coding assistants for suggestions and autocomplete for months — and it worked great. So when a new project came up with a tight deadline, I thought: why not let AI handle the whole backend?\n\nI set up a Cursor workspace, wrote a detailed spec, and hit generate. What followed was 5 days of \"it compiles, but...\" debugging that taught me more about software engineering than any tutorial ever did.\n\nThe boilerplate was genuinely impressive. In about 2 hours, I had:\n\nThe code looked clean. Tests passed. I was feeling like a 10x developer.\n\nThe AI generated this validation:\n\n``` js\nconst userSchema = z.object({\n  age: z.number(),\n});\n```\n\nLooks fine, right? Except the API received ages as strings from the frontend. Zod parsed them fine in development (coercion worked). But in production with stricter mode? `NaN`\n\neverywhere. Users were getting 400 errors on signup.\n\n**Fix:** `z.coerce.number().int().positive()`\n\n— but I had to find all 23 instances manually.\n\nFor a dashboard endpoint that listed users with their orders and order items, the AI generated:\n\n``` js\nconst users = await prisma.user.findMany();\nfor (const user of users) {\n  user.orders = await prisma.order.findMany({ where: { userId: user.id } });\n}\n```\n\nClassic N+1. The Prisma docs literally have a page titled \"How to avoid N+1 queries.\" With 500 users, this endpoint made 501 database queries and took 8 seconds.\n\n**Fix:** `include`\n\nwith nested relations — one query, 120ms.\n\nThe AI wrote a token refresh flow that looked perfect in isolation. But under load, concurrent refresh requests would invalidate each other's tokens. The AI's solution? \"Add a retry mechanism.\" My solution? \"Use a refresh token rotation pattern that handles concurrency properly.\"\n\n```\ncatch (error) {\n  console.log(\"Error:\", error);\n  res.status(500).json({ error: \"Something went wrong\" });\n}\n```\n\n`console.log`\n\ndoesn't serialize Error objects properly. Every production error was just `{}`\n\nin the logs. We ran like this for 3 days before anyone noticed.\n\n**Fix:** `console.error`\n\nwith proper error serialization and a proper logging library (we went with Pino).\n\nHere's what I learned: **AI generates code that's correct in isolation but fragile in context.**\n\nIt doesn't know:\n\nThe generated code passes tests because tests are narrow. It compiles because the syntax is valid. But production is where context matters.\n\n**AI writes the first draft, humans write the final version.** I'm not going back to writing everything from scratch, but every PR now requires a manual review of control flow, error handling, and data access patterns.\n\n**Architecture decisions stay human.** Schema design, caching strategy, and error handling patterns are too context-dependent to outsource.\n\n**Add integration tests that AI can't fake.** Unit tests pass. Integration tests reveal the gaps. We added a test suite that runs the full API against a real Postgres instance.\n\n**Observability from day one.** Structured logging, request tracing, and error tracking are now part of the project template, not an afterthought.\n\nAI didn't break my project. My assumption that \"generated code equals production-ready code\" did.\n\nAI is an incredible force multiplier when used as a pair programmer. It's a liability when treated as a replacement for engineering judgment.\n\nThe week cost me 3 extra days of debugging, but I shipped a more robust system than I would have built alone — because the AI's mistakes taught me where my own blind spots were.\n\nUse AI. But keep your hands on the wheel.\n\n*Have you had similar experiences with AI-generated code? I'd love to hear your war stories in the comments.*", "url": "https://wpnews.pro/news/i-let-ai-write-my-backend-code-for-a-week-here-s-what-actually-broke", "canonical_source": "https://dev.to/kollittle/i-let-ai-write-my-backend-code-for-a-week-heres-what-actually-broke-1d36", "published_at": "2026-06-14 14:02:24+00:00", "updated_at": "2026-06-14 14:40:57.420022+00:00", "lang": "en", "topics": ["artificial-intelligence", "developer-tools", "large-language-models"], "entities": ["Cursor", "Prisma", "Zod", "Pino"], "alternates": {"html": "https://wpnews.pro/news/i-let-ai-write-my-backend-code-for-a-week-here-s-what-actually-broke", "markdown": "https://wpnews.pro/news/i-let-ai-write-my-backend-code-for-a-week-here-s-what-actually-broke.md", "text": "https://wpnews.pro/news/i-let-ai-write-my-backend-code-for-a-week-here-s-what-actually-broke.txt", "jsonld": "https://wpnews.pro/news/i-let-ai-write-my-backend-code-for-a-week-here-s-what-actually-broke.jsonld"}}