Trust but Verify: Sensible Ways to Use LLMs in Production

ai llm development practice management

2025-04-26


Like many engineers right now, I'm exploring how LLMs can accelerate workflows. The potential is undeniable: generating code snippets, drafting content, summarizing complex information, powering chatbots. We are on the cusp of a significant shift in how we build software and create digital experiences. The temptation is strong to integrate these powerful tools directly into our production systems and content pipelines.

But alongside the capabilities come significant risks. LLMs hallucinate. They make factual errors. They perpetuate biases present in their training data, and can be manipulated through prompt injection. Just blindly plugging AI output into user-facing applications or critical systems seems like asking for trouble.

This brings me to a phrase I've been thinking about a lot recently: "trust but verify."

I first heard "trust but verify" a long time ago, but I was surprised to learn its origin. For years, I'd mentally filed it away as wisdom from the cryptography or infosec communities – domains where skepticism is a virtue. It turns out the phrase was popularized by Ronald Reagan during nuclear disarmament talks with the Soviet Union in the 1980s: it represented a pragmatic approach: proceed with the agreement (trust), but ensure mechanisms are in place to check compliance (verify).

That same pragmatism feels incredibly relevant to deploying LLMs today. We want to leverage their power – that's the "trust" part. We see the potential for massive efficiency gains and novel features. Letting an AI draft code, generate product descriptions, or provide first-line customer support can free up human time for higher-level tasks.

However, the "verify" part is absolutely crucial and non-negotiable for anything going into production. Raw, unmediated LLM output is rarely trustworthy enough on its own. Hallucinations aren't just funny quirks when they manifest as incorrect information presented to someone who relies on you.

How do we actually "verify" AI-generated output in a production context? It's a layered approach:

Implementing verification adds friction. It requires building additional systems, dedicating human time to oversight, and accepting that deployment might be slower than just letting the AI run wild. But it is also the only responsible way forward. Harness the benefits and mitigate the risks.

"Trust but verify" isn't about stifling innovation; it's about enabling and guiding it sustainably. As LLMs continue to evolve, perhaps the nature of verification will change, but the principle will likely remain.