In today’s AI-powered software world, large language models (LLMs) have become a go-to tool for generating code snippets, APIs and even full applications. It’s fast, it’s magical and sometimes, it’s dangerous.
A thought crossed my mind recently: Are we witnessing the rise of “code vomit”?
Code vomit happens when code is generated without discipline, without understanding the system it’s going into, the environment it must survive in, or the humans who will maintain it.
Yes – LLMs are incredibly helpful. At Refraime we actively use AI to accelerate our development cycles. But we’ve learned (sometimes the hard way) that generating code does not equal building a system.
Why Code Understanding Matters More Than Ever
LLM-generated code may run, but that’s not the same as being:
- performant under real-time constraints
- secure in a threat-prone environment
- maintainable by future teams
- compliant with evolving standards
- deployable across edge, cloud or hybrid infrastructure
In production environments, especially where AI is used for real-time decision-making, (like security alerts, camera analytics, or collision detection) your code must think ahead, not just run now. Anticipating future demands, edge cases and operational realities, rather than writing code that only works in the immediate moment or narrow test conditions.
Code that thinks ahead means:
- Anticipating Scale and Load
- Short-sighted: A script works on one camera feed.
- Thinks ahead: Code is asynchronous, memory-efficient and scales to handle hundreds/thousands of feeds without GPU overload or latency spikes.
Planning for Change
Short-sighted: Hard-coded values, brittle logic.
Thinks ahead: Uses config-driven architecture, modular design, version control for models and pipelines.
Considering Failure Modes
Short-sighted: Assumes everything always works (e.g., DB always responds).
Thinks ahead: Implements retry logic, timeout handling, circuit-breakers and graceful degradation.
Being Operator-Friendly
Short-sighted: Cryptic logs, no observability.
Thinks ahead: Structured logs, meaningful alerts, clear metrics for ops teams to debug quickly.
Fitting the Real Deployment Environment
Short-sighted: Runs fine locally or in a Colab notebook.
Thinks ahead: Accounts for edge-compute limits, bandwidth constraints, cold-start of models, container orchestration and production CI/CD pipelines.
Security & Compliance Awareness
Short-sighted: Logs sensitive data, uses plain-text credentials.
Thinks ahead: Encrypts sensitive information, follows least-privilege principles, aligns with regulatory standards (e.g., POPIA in South Africa, GDPR globally).
Code that thinks ahead is production-aware, adaptable and resilient. It’s not just about solving the problem in front of you – it’s about setting your future self (or team) up for success when the stakes are higher and the context is messier.
Take-aways for us at Refraime and similar high-stakes environments:
- Relying purely on speed and “just generate” can yield technical debt, security debt and future fragility.
- Even though AI code seems “correct” and compiles/runs, we must assume it might contain design flaws, security gaps or unsuitable architecture.
- The human-in-the-loop and architecture-first mindset is more essential than ever: We need to treat LLMs as assistive rather than autonomous code-writers.
Don’t Let AI Be Your Architect
We often see a “prompt-in, code-out” mindset. Developers generate code without fully understanding what it does. It works…until it fails under pressure.
At Refraime, we enforce several practices:
- Architect first, generate later. Understand the problem, map out the system, define the interfaces. Then use LLMs to fill in parts.
- Refactor aggressively. Treat LLM-generated code like junior-developer output: review it, refactor it, apply design standards, unit tests, integration tests.
- Context is king. Provide models with meaningful input: system constraints, memory/bandwidth limits, deployment targets, latency budgets, failure modes.
- Code is communication. Your code isn’t just for the machine – it’s for the person who’ll debug it at 2 AM when things go wrong.
When to Trust the LLM
You can use LLMs confidently, but knowing when is key.
Use LLMs for:
- Boilerplate generation (scaffolding modules, CRUD endpoints)
- Rapid prototyping (proofs of concept, exploring options)
- Refactoring suggestions (improving naming, modularisation, readability)
- Deployment configuration scaffolding (CI/CD pipelines, container templates)
- Starting points for solving complex problems (but only with heavy human oversight)
Avoid relying on LLMs without human oversight in situations where:
- Security is critical (e.g., camera analytics in surveillance, threat detection)
- Compliance and regulatory standards apply (POPIA, GDPR, critical infrastructure)
- Performance, latency or real-time constraints dominate (edge deployments, low latency)
- Systems are safety-critical (collision detection, autonomous decisions)
Final Thought
LLMs are here to stay. They are revolutionising how we build. But speed without understanding leads to fragility. If we don’t slow down and think critically about what we’re building, we risk turning AI into a code-spitting vending machine.
Let’s not ship “code vomit.”
Let’s ship systems, thoughtfully engineered, tested and ready for the real world.
Have you seen “code vomit” in the wild?
Or are you refining how your team integrates LLMs responsibly?
Drop a comment – I’d love to hear how others are navigating this.
