Stop Shipping AI Slop: Build an Anti-Slop Harness Around Your LLM

Mehmet TURAÇ Posted on May 30 Stop Shipping AI Slop: Build an Anti-Slop Harness Around Your LLM # ai # llm # architecture # engineering "AI slop" is not a model problem. It's an engineering problem you decided not to solve. The slop is the bland, off-voice, half-hallucinated, occasionally-just-an-error-message text that your LLM emits maybe 5% of the time — and that 5% is the part users screenshot. The instinct is to fix it in the prompt: add three more sentences of "be concise, be accurate, match my tone." That treats a stochastic system as if it were deterministic. It isn't. You cannot prompt your way to a guarantee. What actually works is treating the model like any other unreliable upstream dependency: wrap it in a harness that validates, rejects, and retries before anything reaches a user. The model proposes; the harness disposes. Here's how to build one. Slop is a systems problem, not a prompt problem Every production LLM feature I've shipped converged on the same shape: the model is one stage in a pipeline, not the pipeline itself. You don't trust raw generation any more than you'd trust raw user input. You parse it, you validate it against constraints you can express in code, and you reject anything that fails — automatically, before a human ever sees it. The key insight is that most slop is detectable . Empty output, a leaked stack trace, the wrong language, a 900-word answer when you asked for 200, a banned phrase like "in today's fast-paced world" — these are all checkable with deterministic code. You don't need a judge model to catch them (though a judge model has its place at the end). You need a gate that runs on every generation, costs microseconds, and never gets tired. Think of it as five layers, each rejecting a different class of failure. Layer 1: Structured output, not freeform text The single biggest reduction in slop comes from refusing to accept prose where you can demand structure. If you ask for a JSON object with named fields and a schema,