Dhyey Mavani

Traditional software governance often uses static compliance checklists, quarterly audits and after-the-fact reviews. But this method can't keep up with AI systems that change in real time. A machine learning (ML) model might retrain or drift between quarterly operational syncs. This means that, by the time an issue is discovered, hundreds of bad decisions could already have been made. This can be almost impossible to untangle. 

For decades, we have adapted to software. We learned shell commands, memorized HTTP method names and wired together SDKs. Each interface assumed we would speak its language. In the 1980s, we typed 'grep', 'ssh' and 'ls' into a shell; by the mid-2000s, we were invoking REST endpoints like GET /users; by the 2010s, we imported SDKs (client.orders.list()) so we didn’t have to think about HTTP. But underlying each of those steps was the same premise: Expose capabilities in a structured form so others can invoke them.

Dhyey Mavani is accelerating generative AI and computational mathematics and is a guest author at VentureBeat.

Large language models (LLMs) have astounded the world with their capabilities, yet they remain plagued by unpredictability and hallucinations – confidently outputting incorrect information. In high-stakes domains like finance, medicine or autonomous systems, such unreliability is unacceptable.

As more companies quickly begin using gen AI, it’s important to avoid a big mistake that could impact its effectiveness: Proper onboarding. Companies spend time and money training new human workers to succeed, but when they use large language model (LLM) helpers, many treat them like simple tools that need no explanation.