Before your logic branches into multiple paths, log the entry point of the process.

This gives you a clear starting point for tracing execution and a reliable anchor when reconstructing flows during debugging. Without this, you're often guessing where things began.

Whenever your system decides not to do something, log it. Silent skips are dangerous: nothing breaks, but something doesn't happen, and the absence of output is invisible.

Always include full context when logging skips: why it was skipped, what conditions led to it, and any relevant identifiers.

It's tempting to only log failures, but success logs are just as important. They confirm expected behaviour, help compare "working" vs "broken" flows, and make metrics and auditing easier.

A system that only logs failures tells an incomplete story.

If input doesn't match expectations but the system can still proceed, log a warning. This covers missing optional fields, unexpected formats, and partial data inconsistencies.

This is your early warning system before things escalate into errors.

If your system applies non-obvious rules — especially ones that aren't symmetric — log them explicitly. Special-case handling, priority overrides, and hidden business logic should surface in logs rather than be applied silently.

This will help future you, and your teammates.

In systems that trigger multiple downstream actions — batch jobs, fan-out events — logging each failure individually isn't enough. Add a summary log of aggregated failures with context about the overall operation.

This helps you answer "did the whole operation succeed?" not just "did individual parts fail?"

Sometimes you need both high-level logs for overall job or request outcome, and detailed logs for step-by-step execution. Design your logging so you can stay at a high level for normal operations and zoom into specific jobs when debugging.

This layered approach prevents noise while preserving depth.

Every log entry should include a minimum consistent set of fields — an id (request ID, job ID, correlation ID) and a source (service or module name) at the very least.

Consistency enables reliable filtering, easier querying in log systems, and better observability across services. Structured logging — JSON logs — is a big win here.

Not all logs are equal. Misusing severity levels makes logs harder to trust. A simple guideline: INFO for normal system behaviour, WARN for recoverable or expected issues, ERROR for unexpected failures or system misconfigurations.

If everything is an error, nothing is.

Avoid logging sensitive data — tokens, passwords, personal information. Prefer context over verbosity: a short, meaningful log beats a long useless one.

Think in queries: "How would I search for this problem later?" Use correlation IDs across services to trace distributed flows. And periodically review your logs — logging quality degrades over time if left unchecked.