Building Reliable AI Applications

Large language models are probabilistic components. The same input can produce different outputs, quality varies across inputs, and a provider can change behaviour underneath you. None of that makes reliable software impossible — it means reliability has to be designed into the system around the model rather than expected from the model itself.

Treat the model as an unreliable dependency

In distributed systems we already know how to work with dependencies that can be slow, wrong, or unavailable: timeouts, retries, validation, fallbacks, and monitoring. An LLM call deserves the same treatment.

• Timeouts and retries with backoff, and an explicit budget for how long a user is willing to wait.
• A defined failure state that the UI can render honestly, instead of showing a half-parsed answer.
• Version pinning for models and prompts, so that a change in behaviour is a deliberate deployment rather than a surprise.

Define contracts with structured output

Free-form text is hard to test and hard to consume. Wherever the output feeds other code, ask for structured output and validate it at the boundary.

import { z } from "zod";
 
const Finding = z.object({
  severity: z.enum(["info", "warning", "critical"]),
  message: z.string().min(1),
  evidence: z.string().optional(),
});
 
const AnalysisResult = z.object({ findings: z.array(Finding) });
 
export function parseAnalysis(raw: unknown) {
  const result = AnalysisResult.safeParse(raw);
  return result.success ? { ok: true as const, value: result.data } : { ok: false as const, error: result.error };
}

A schema turns "the model said something odd" into a typed, observable failure that can be counted, retried, or surfaced to the user.

Separate deterministic logic from model judgement

Not everything needs a model. Input normalisation, permission checks, formatting, routing, and many business rules are deterministic and should stay in ordinary, unit-tested code. Use the model for the parts that genuinely require language understanding, and keep that surface as small as possible.

This separation pays off in testing: the deterministic majority of the system can be covered by fast, conventional tests, while evaluation effort concentrates on the model-dependent core.

Build feedback loops before you need them

Reliability is maintained, not achieved once. Useful loops include:

1. An evaluation suite of representative examples that runs on every prompt or model change.
2. Production telemetry: latency, error and validation-failure rates, token usage, and user feedback signals.
3. A path from production back to evaluation, so that real failure cases become regression tests.

Keep a human in the loop where it matters

For decisions with real consequences, present model output as a suggestion that a person reviews. Design the interface so that accepting, editing, and rejecting are equally easy, and make it clear which parts were generated.

Summary

Reliable AI applications come from familiar engineering discipline applied to an unfamiliar component: clear boundaries, explicit contracts, deterministic code where possible, and continuous evaluation. The model is one part of the system — the reliability belongs to the whole.