Smart LLM Usage: Avoiding Common Pitfalls

The AI automation space is exploding with creativity. Every day I see impressive examples of what people are building with LLMs—from simple integrations to complex agent systems that handle real business logic. The enthusiasm is fantastic, and the barrier to entry has never been lower.

But as the ecosystem matures, I’m noticing patterns of usage that concern me. Not because the technology can’t handle these use cases, but because we’re collectively developing habits that will become harder to break as these systems scale. We’re teaching a generation of developers and enthusiasts practices that work fine for demos but fall apart in production.

Let me share three patterns I see frequently that deserve more scrutiny.

The Three Common Pitfalls

1. Using LLMs for Everything

I recently watched a tutorial (from someone with supposedly extensive industry experience) demonstrating an AI agent that performs health checks on API endpoints every 5 minutes. The agent calls an LLM, which then decides whether to ping the endpoint and interpret the response.

Here’s the thing: every LLM call costs money and has environmental impact. A simple health check can be a direct HTTP request—if you get a status code 200, you’re good. No reasoning required, no natural language processing needed.

That simple change:

  • Saves money (orders of magnitude cheaper)
  • Is better for the environment
  • Runs significantly faster
  • Is more reliable (fewer moving parts)
  • Could run every minute instead of every 5 minutes and still be cheaper

LLMs are powerful tools for complex reasoning, natural language processing, and creative tasks. They’re not meant to replace basic HTTP requests, simple logic checks, or standard automation scripts.

2. Giving LLMs Free Rein on Critical Systems

Another tutorial showed an LLM given full access to a production server to “do whatever it deems necessary” to restart a stopped service. No constraints, no approval workflow, no audit trail of what it might do.

Think about explaining that to your boss or customer when something goes wrong:

“I have no idea what happened!”

That won’t cut it.

Even worse, when you’re creating tutorials or showcasing systems, you’re setting an example. People perceive tutorial creators as knowledgeable and trustworthy. They’ll replicate your patterns—including the dangerous ones—with potentially nasty consequences.

Production systems need:

  • Clear boundaries on what actions are permitted
  • Approval workflows for critical operations
  • Comprehensive logging and audit trails
  • Ability to understand and explain every action taken

3. Skipping Engineering Fundamentals

I see lots of content creators building genuinely cool stuff while completely skipping software engineering fundamentals like version control and backups. These aren’t just nice-to-have practices—they’re essential infrastructure that prevents disasters.

The challenge isn’t that creators are intentionally teaching bad practices. Many are enthusiastic builders sharing what they’ve learned. But there’s a knowledge gap, and the tutorial ecosystem needs more voices emphasizing production-ready patterns, not just what works for a demo.

What Should You Do About It?

So how do we use LLMs smartly? Here are practical guidelines:

Ask yourself: Do I really need an LLM for this?

Before reaching for an AI agent, consider if there’s a simpler, more efficient solution. LLMs excel at complex reasoning, natural language processing, and creative tasks. They’re expensive overkill for basic HTTP requests, simple logic checks, or standard automation scripts. Use the right tool for the job.

Implement guardrails and observability

If you give an LLM access to systems, ensure you have:

  • Proper logging of all actions
  • Approval workflows for critical operations
  • Clear constraints on what the LLM can and cannot do
  • Audit trails showing what was done and why

You should always know what actions were taken and why. Your future self (and your boss) will thank you.

Embrace software engineering fundamentals

Version control isn’t optional—it’s essential. Whether you’re building in n8n, Python, or any other platform, your code should be tracked, versioned, and backed up.

Essential practices:

  • Set up proper Git workflows
  • Create meaningful commit messages
  • Maintain backups
  • Test before deploying to production

These practices aren’t just for “serious” developers—they’re for anyone who builds software that has value.

Learn from reliable sources

When learning about AI implementation, be critical of the tutorials you follow. Ask yourself:

  • Is this person following best practices?
  • Are they considering costs, security, and maintainability?
  • Is this approach production-ready, or just demo-ready?

Just because someone has a popular YouTube channel doesn’t mean their approach is production-ready. Look for creators who discuss trade-offs, limitations, and engineering fundamentals alongside the exciting capabilities.

Think long-term

Quick demos and proofs-of-concept are great for learning, but production systems need different considerations:

  • What happens when your LLM calls cost more than your infrastructure?
  • What happens when an AI agent makes an unexpected decision?
  • How will you debug issues in production?
  • Can you explain the system’s behavior to stakeholders?

Plan for scale, cost, and accountability from the start. It’s much easier to build these considerations in early than to retrofit them later.

Building a Sustainable AI Ecosystem

The excitement around LLMs is justified—they’re transformative technology. But as we build the practices and patterns that will define this space for years to come, we need to balance enthusiasm with engineering discipline.

We need more voices in the tutorial ecosystem who:

  • Demonstrate production-ready patterns, not just proof-of-concepts
  • Discuss when NOT to use LLMs as much as when to use them
  • Emphasize fundamentals like version control, testing, and observability
  • Show the trade-offs and real costs, not just the capabilities

If you’re creating content or building systems with LLMs, you have an opportunity to help shape these practices. Choose wisely—others are watching and learning from what you build.

What patterns have you seen that concern you? What best practices have you found valuable? I’d love to hear your experiences in building with LLMs—both the successes and the lessons learned the hard way.