5 LLM Integration Pitfalls That Will Kill Your Production App

Integrating an LLM into a production app is not hard. Integrating one that stays reliable, affordable, and secure under real traffic is. Here are the five mistakes we see in nearly every first-time LLM integration.

Pitfall 1: No token budget. Every request without a max_tokens cap is a ticking cost bomb. A single runaway prompt can cost more than your entire daily budget. Always set explicit limits and model the worst-case token consumption before you ship.

Pitfall 2: Trusting model output blindly. LLMs hallucinate. If your app takes actions based on model output — writes to a database, sends emails, calls APIs — you need output validation. JSON schema validation with Zod or a structured output library is non-negotiable.

Pitfall 3: No retry strategy with backoff. Provider APIs go down and rate limit unexpectedly. A bare fetch call with no retry logic will surface as user-visible errors under any real load. Implement exponential backoff with jitter from day one.

Pitfall 4: Prompt injection exposure. If you interpolate user input directly into prompts without sanitisation, you have a prompt injection vulnerability. Attackers can override your system prompt, leak context, or manipulate outputs. Separate system and user content and treat user input as untrusted.

Pitfall 5: No cost monitoring. Add cost tracking from the start. Log token usage per request, alert when daily spend exceeds a threshold, and tie usage to specific features so you know which ones are expensive.