The Most Common API Mistakes in AI Apps — And How to Avoid Them

AI apps rely heavily on external APIs — mainly LLM providers. But small API mistakes can cause huge issues: broken flows, cost spikes, inconsistent responses, or app crashes.

Here are the most common API mistakes vibe coders make and how to avoid them.

Mistake 1: Using the Wrong Model for the Task

Not every model is built for:

• long output
• structured output
• extraction
• reasoning
• code generation

Choose the model that fits the job, not the one that sounds powerful.

Mistake 2: No Output Token Limits

Without limits, LLMs ramble. This leads to:

• unexpected costs
• overflowed outputs
• broken formatting

Always set a hard output limit.

Mistake 3: Misconfigured Temperature Settings

High temperature = creative Low temperature = stable

Most AI apps need stability.

If your app is inconsistent, check your temperature settings.

Mistake 4: Not Handling Timeouts or Slow Responses

LLM APIs can timeout under load.

Make sure your app:

• retries
• handles partial responses
• shows safe error messages
• avoids exposing system info

Mistake 5: Not Validating the Response Format

An API call is only as good as the structure it returns.

Validate:

• field presence
• data types
• JSON validity
• model consistency

Mistake 6: Leaking API Keys Through Error Messages

Never echo error messages directly to the user. They may contain:

• keys
• configs
• internal logs

Sanitize everything.

Mistake 7: Unnecessary Repeated API Calls

Too many calls lead to:

• slow performance
• high costs
• redundant processing

Optimize your request flow.

Final Thought: API Issues Are Preventable

Most API problems can be avoided with a basic review. A few checks go a long way toward stable app behavior.