Logging & Observability: Engineering for Vibe Coders
Vibe coding makes it easy to build prototypes fast. But fast prototypes often fail silently. Something goes wrong, and you have no idea why. That is where logging and observability come in.
Logging and observability are the “eyes and ears” of your application. They help you understand what your prototype is doing, what users are experiencing, and why errors happen. Even if your prototype is simple, planning for visibility early saves hours of frustration later.
1. Why logging matters
Without logging, your prototype is a black box. You might see the output, but you don’t know what happened internally. Problems that are easy to reproduce for you may be impossible to debug for others.
Logging helps you:
- Track user interactions and inputs
- Identify which code paths are being executed
- Detect failures early
- Collect metrics for performance monitoring
Even minimal logging can drastically reduce the time it takes to understand and fix issues.
🟢 Pre-prototype habit:
Before coding, decide what events and actions you need to observe. Consider user inputs, API calls, and critical function outputs. Sketch out which logs will help you debug quickly.
2. Types of logs and how to use them
Not all logs are equal. Good logs are structured and meaningful, not just random print statements. Common categories:
- Info: general workflow steps, e.g., “User submitted form”
- Warning: unexpected situations that aren’t critical, e.g., “Missing optional field”
- Error: failures that require attention, e.g., “API call timed out”
- Debug: detailed information for developers, often disabled in production
Structured logging, with consistent keys and values, allows easy filtering, searching, and analysis, especially for larger prototypes or production deployments.
🟢 Pre-prototype habit:
Plan a simple logging format before coding. Decide which events belong in each category and where logs will be stored or displayed.
3. Observability beyond logs
Observability is broader than logging. It’s the ability to understand your system’s state through metrics, tracing, and events.
Key considerations:
- Metrics: track numeric data like response time, request count, or memory usage
- Tracing: follow a request or action as it moves through multiple functions or services
- Alerts: notify you when something unusual happens (e.g., error rates spike)
Even a lightweight observability plan helps you catch problems that simple logging cannot.
🟢 Pre-prototype habit:
Identify key metrics that indicate success or failure for your prototype. Decide how you’ll track them and what thresholds matter.
4. Logging and observability for AI prototypes
AI prototypes introduce unique challenges:
- Model responses may be unpredictable
- API calls can fail or return unexpected outputs
- User interactions may generate high variability
In these cases, logging and observability let you:
- Capture raw inputs and model outputs for debugging
- Detect performance or response anomalies
- Replay sessions to understand edge cases
Even small prototypes benefit from logging model responses and API calls.
🟢 Pre-prototype habit:
Plan which inputs, outputs, and events you will log for AI models and API calls. Think about what would help you reproduce or understand errors.
5. Quick pre-prototype checklist
| Checklist Item | Why It Matters |
|---|---|
| Identify events to log | Ensures visibility into important actions and failures |
| Define logging levels | Separates routine info from warnings and errors |
| Plan storage or display for logs | Makes logs accessible for debugging and monitoring |
| Identify metrics for observability | Provides insight beyond simple logging |
| Decide how to trace critical workflows | Helps reproduce issues and understand flow |
| Plan lightweight alerts | Detect issues early without manual monitoring |
Closing note
Logging and observability are not just for production-ready apps. They are essential even in prototypes to understand behavior, troubleshoot problems, and prepare for scaling.
🟢 Pre-prototype habit:
Before coding, sketch out which actions, inputs, and events you will log, which metrics matter, and how you will track them. Think about how this visibility will help you fix problems faster and build a more reliable prototype.
