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:
- Human Review: For critical outputs (e.g., code affecting core logic, sensitive communications, definitive factual statements), there's is no substitute for a knowledgeable human reviewing the AI's work before it goes live.
- Automated Checks: Just as we write unit tests for human-written code, we need tests for AI-generated code. For generated content or data, validation rules, fact-checking against known databases, or checks for PII or toxicity can be automated.
- Monitoring and Feedback Loops: Log your LLM interactions and outputs. Track performance. Implement mechanisms for users to flag incorrect or unhelpful responses (a simple thumbs up/down is more than enough). Crucially, have a system in place to act on this feedback quickly.
- Sandboxing and Staging: Test LLM-powered features thoroughly in non-production environments before rolling them out. Understand their failure modes in a safe space. This should be obvious but you'd be surprised how many people are manipulating prompts in their live environment and hoping for the best.
- Clear Boundaries and Fallbacks: Define where AI is used and where it isn't. Everyone in your organisation needs to understand these boundaries. CEO to product to engineer. Have robust fallback mechanisms for when the AI fails or provides low-confidence answers. Don't let the AI operate outside its known competence.
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.