Core Bottleneck In AI Engineering Isn't Writing Code. It's Trusting What Code Produces

AI engineering teams are no longer bottlenecked by writing code — the real challenge is trusting what LLMs produce. Using a medical chatbot for implantology as a case study, this post details an evaluation and observability stack built around Promptfoo and Langfuse. The approach includes creating domain-specific golden datasets, out-of-scope refusal datasets, adversarial no-manufacturer and no-direct-instructions datasets, and a hallucinations dataset. Automated metrics like Context Faithfulness and Answer Relevance enable regression testing at scale without requiring domain experts to review every output. Performance testing under concurrent load revealed rate limit and vector database bottlenecks. The next step is applying the same evaluation harness to live production traffic for continuous monitoring. The framework is now standard across all AI engagements at Netguru.