Users see “Error 500: Internal Server Error” and have no idea what to do. Developers see a stack trace and know exactly what happened but do not translate that into user-friendly guidance. The gap between what the system knows about the error and what the user sees is enormous.

The AI Approach

An LLM takes the technical error context - error code, stack trace, request parameters, and the user’s recent actions - and generates a plain-language explanation of what went wrong and what the user can try next.

Three-Step Build

  1. Capture context - When an error occurs, collect the error type, message, relevant request parameters (sanitized of sensitive data), and the user’s recent actions or page context.
  2. Generate message - Send the technical context to an LLM with a prompt that produces a user-friendly message: what happened in plain language, whether it is the user’s fault or a system issue, and what they should try next.
  3. Display - Show the friendly message to the user. Include a reference ID that links to the full technical details for support and debugging.

Where It Breaks

Calling an LLM during error handling adds latency and introduces a dependency that could itself fail. Pre-generating messages for common error types and caching them mitigates this. Sensitive error details could leak into user-facing messages if the prompt is not carefully designed.

The Production Path

Start by using AI to generate a library of friendly messages for your most common error codes, reviewed by a human. Serve these from a lookup table with zero LLM latency. Reserve real-time LLM generation for rare or novel errors. Track which error messages lead to successful resolution versus repeated support tickets.