Build Reliable
AI Agents

The inherent non-determinism of AI systems creates a major hurdle: despite my best efforts, I find it difficult to guarantee a consistent experience for my users, as LLMs often provide different answers to the same question.

Development has evolved: coding the application is now secondary to the critical task of extensive testing. Our main focus is minimizing AI hallucinations and preparing for every conceivable edge case to ensure a robust and reliable product.

Creating a shortlist of two models is easy, thanks to media coverage. However, the true test begins when you spend weeks meticulously differentiating between them, only to realize that identifying the perfect fit requires a deep dive into their nuances and capabilities.

We're stuck in a never-ending loop of trial and error, with no clear assurance of success. Every time I choose a model, I'm plagued by doubts: could a different prompt on another model produce a superior answer? The uncertainty is exhausting.

Every week brings news of a new model, yet I find myself overwhelmed and unable to test these promising LLMs. Despite their potential to significantly elevate my application's quality and cut costs, I simply don't have the resources to explore them.

Every time a new model is released, I face a challenge: how to guarantee that the upgrade won't degrade the quality of the answers, and whether I should adjust my prompts to maintain performance.