AI Prompts for Error Handling

Add error handling to this function that fetches user data.

Most error-handling advice is wrong about the goal. It treats error handling as catching every exception so nothing crashes — wrap everything in try/catch and move on. But a program that catches every error and continues silently isn't resilient; it's a program that hides its own failures until they've done real damage somewhere downstream. The actual goal of error handling is failing usefully — surfacing problems with enough context to act on them. AI prompts for error handling need to aim for that, because the model's default is often the silent-swallow anti-pattern.

What Are AI Prompts for Error Handling?

AI prompts for error handling are structured requests that get a model to generate code which responds to failures deliberately — with meaningful error messages, appropriate recovery, and enough context to debug — rather than either crashing unhelpfully or swallowing errors into silence.

Good error handling is a spectrum between two failures. On one end, code that doesn't handle errors at all crashes with cryptic stack traces. On the other, code that catches everything and continues hides problems until they surface as corrupted data or confused users. The sweet spot — deliberate handling that fails loud where it should and recovers where it can — is what you have to prompt for specifically.

⚡ Pro tip: Ask the model to distinguish between errors it should recover from and errors it should let propagate. Not every error should be caught. A network timeout might warrant a retry; a programming bug like a null reference should crash loudly so you fix it. Blanket try/catch erases this distinction and hides the bugs you most need to see.

Why It Matters

The cost of bad error handling is paid in debugging time and silent data corruption. When code swallows errors, problems don't disappear — they move. A failed database write that's caught and ignored means a user thinks their data saved when it didn't. A caught-and-logged-nowhere exception means a feature silently stops working and nobody knows until a customer complains. These are the worst bugs to trace because the failure and the symptom are far apart.

Good error handling, by contrast, makes failures visible and actionable. An error that says "failed to charge card for order 4821: payment gateway timeout" tells you what broke, where, and why. You can act on it immediately. The difference between that and a generic "something went wrong" is hours of debugging.

And honestly, the contrarian point bears repeating: the instinct to catch everything comes from a good place — nobody wants crashes — but it's misguided. A crash with a clear stack trace during development is a gift; it points straight at the bug. Swallowing that crash to avoid seeing it just moves the pain to production, where it's far more expensive to diagnose.

Prompting for Meaningful Errors

The core of good error handling is context. An error should tell you what operation failed, with what inputs, and why.

Here's a weak error-handling prompt and its result:

Add error handling to this function that fetches user data.

You'll likely get a try/catch that logs "error occurred" and returns null — technically handled, practically useless. Now the strong version:

Add error handling to this user-fetch function. Requirements: - Distinguish error types: network failure (retry-able), 404 (user genuinely not found), 500 (server issue), and unexpected errors. - Each error message includes: the operation, the user ID, and the specific cause. - Retry network failures with exponential backoff (max 3), but don't retry 404s. - Let truly unexpected errors propagate rather than swallowing them. - Log with enough context to debug, but never log sensitive data. Explain which errors you recover from and which you don't.

What this does: It forces the model to categorize errors and respond appropriately to each — retrying what's transient, surfacing what's not, and including debugging context — instead of catching everything into a useless generic handler.

⚡ Pro tip: Require that every error message name the operation and the relevant identifiers. "Failed to load user 4821: connection timeout" is debuggable; "an error occurred" is not. The identifiers are what let you reproduce and trace the problem instead of guessing.

Prompting for Smart Recovery

Recovery should match the error. Transient errors warrant retries; permanent ones don't. The prompt should make this explicit.

For this API client, implement retry logic that: - Retries on network errors and 5xx responses (transient) - Does NOT retry on 4xx responses (client errors won't fix themselves) - Uses exponential backoff with jitter to avoid thundering herd - Gives up after 3 attempts and surfaces a clear final error Explain why 4xx errors aren't retried.

What this does: It encodes the crucial distinction between transient and permanent failures, so the code retries what might succeed and immediately surfaces what won't — instead of hammering a doomed request or giving up on a recoverable one.

A backend engineer I know says the "retry 5xx but not 4xx" distinction is the single most common thing generated retry logic gets wrong — it either retries everything (uselessly hammering endpoints that return 400) or nothing (giving up on transient blips that a retry would fix).

⚡ Pro tip: Always ask for jitter in retry backoff. Without it, many clients that failed at the same moment retry at the same moment, creating a "thundering herd" that overwhelms a recovering server. Jitter spreads the retries out and is trivial to add when named.

Common Mistakes

The biggest mistake is the empty or generic catch block — catch (e) { } or catch (e) { console.log('error') }. This swallows the error, hides the problem, and often leaves the program in an inconsistent state. Every catch should do something meaningful: handle, recover, re-throw with context, or at minimum log with enough detail to act on.

⚠️ Common mistake: Catching an exception and continuing as if nothing happened. Silent failure is worse than a crash because it corrupts state invisibly. If code can't meaningfully recover from an error, it should let the error propagate rather than swallow it and pretend success. A loud failure you can see beats a quiet one you can't.

Another frequent error is catching errors too broadly. A try/catch wrapping fifty lines catches errors from all of them indiscriminately, making it impossible to know what actually failed or to handle different failures differently. Catch narrowly, around the specific operation that can fail.

A subtler mistake is logging sensitive data in error handlers. In the rush to add debugging context, generated code sometimes logs the full request including passwords or tokens. Error context should be rich but scrubbed of secrets.

Conclusion

AI prompts for error handling succeed when they aim for useful failure, not silent survival. Categorizing errors, including debugging context, retrying only what's transient, and letting real bugs propagate are what turn error handling from a way to hide problems into a way to surface and solve them.

The developers whose code is easy to debug aren't catching more errors; they're handling them more deliberately. PromptABCD is where those patterns live — save your error-categorization prompt, your transient-vs-permanent retry template, and your context-rich logging skeleton, then reuse them across your code. Error handling is worth getting right, because the difference between "failed to charge order 4821: gateway timeout" and "something went wrong" is the difference between a five-minute fix and a five-hour hunt. Aim for failures that tell you what to do next, and your future debugging self will be grateful.

The mindset shift that makes all of this click is treating errors as information rather than nuisances. A well-designed error is a message from your code to your future self, delivered at the exact moment something went wrong, carrying everything needed to understand and fix it. When you think of it that way, the empty catch block reveals itself as what it really is: throwing away a message you'll desperately wish you had later. Every choice in good error handling — the context, the categorization, the deliberate decision to recover or propagate — flows from respecting errors as valuable signal instead of noise to suppress.

⚡ Pro tip: When reviewing generated code, search it for empty catch blocks and catches that only log without acting. These are the silent-failure landmines. Each one is a place where a future problem will hide instead of announcing itself, and turning them into meaningful handling — or deliberate propagation — is among the highest-value edits you can make to any codebase.

This reframing changes how you review code, too. Instead of asking "does this handle errors?" — a yes/no question that a blanket try/catch answers deceptively — you start asking "when this fails, will I be able to tell what happened?" That question exposes the difference between error handling that helps and error handling that merely exists. A generic catch technically handles errors; it just handles them by destroying the information you'd need. Once you're asking the better question, the weak patterns become obvious, and the habit of demanding context and deliberate recovery in your prompts follows naturally from it. Ask the better question in every review, and your codebase steadily fills with failures that explain themselves instead of failures that hide. That is the quiet compounding benefit of treating every error as a message worth keeping.