Treat the LLM as an external dependency
An LLM call should be wrapped like any external service: timeout, error handling, volume control and fallback behavior.
Business logic should not depend on free-form text that cannot be validated. Structured outputs significantly reduce the error surface.
The AI service should remain behind a clear interface so the rest of the backend is not directly coupled to one provider SDK.
Validate outputs before using them
Even when a model returns JSON, the backend should consider that output untrusted until validated.
Schemas, constraints, enums, maximum lengths and required fields should be checked exactly like any external request.
Invalid output should trigger controlled retry, fallback or human validation, never silent persistence.
Minimize transmitted data
The backend layer should select only the information required for the use case and mask sensitive data whenever possible.
This approach reduces security risk, call cost and governance complexity at the same time.
Internal identifiers, tokens, secrets and fields unrelated to the task should never be included in prompts by default.
Manage timeouts, retries and circuit breakers
An AI provider can slow down, rate-limit or become temporarily unavailable. The backend should use short timeouts and bounded retry strategies.
A circuit breaker prevents threads or requests from piling up when an external service is already failing.
Non-interactive processing can be moved to a queue or asynchronous worker to protect primary API response times.
Measure cost, latency and quality
Every important AI call should produce metrics: model, duration, approximate token count, status and task type.
These metrics help detect cost drift, performance regressions and unexpected usage.
Quality should also be monitored using representative test cases rather than occasional impressions.
