Many tech professionals see integrating large language models (LLMs) as a simple process -just connect an API and let it run. At Wallarm, our experience has proved otherwise. Through rigorous testing and iteration, our engineering team uncovered several critical insights about deploying LLMs securely and effectively. This blog shares our journey of integrating cutting-edge AI into a security product. It’s also a testament to the Wallarm engineers who tackled each challenge head-on, often working with technology that wasn’t ready-made or safe by default. I’m grateful for their dedication. If you’re an engineering leader or an AI practitioner navigating similar complexities, hopefully our experiences can provide some guidance. 1. The Myth of the Perfect Prompt Early on, we wanted to believe in the myth of the “perfect prompt.” Write it well enough, and your LLM will answer anything accurately, right? Unfortunately, the reality is that even the best prompt, for the simplest task, will still get things wrong. Sometimes hilariously. Sometimes dangerously. In security, a single miss means a threat slipping through. That’s why we never settled for “one and done.” Our engineers built pipelines where every LLM output is validated multiple times, often by additional models and adversarial modules. We drew inspiration from ensemble theory and backed up our work with the latest research from Microsoft, DeepMind, and others like Reflexion, and AutoGPT. For example, when…Read More