As organizations rush to adopt Large Language Models (LLMs), many discover that building reliable, trustworthy applications is far from straightforward. Unlike traditional software, LLM outputs are non-deterministic, context-dependent, and vulnerable to issues like bias, hallucinations, and prompt injection. Ensuring quality requires more than testing—it demands a holistic approach that blends architecture, safety, observability, and continuous feedback. This talk explores practical strategies for embedding quality into LLM-powered systems from the ground up. We’ll cover methods for prompt design, evaluation frameworks, guardrails, and hybrid architectures that improve accuracy and safety. Attendees will leave with a clearer understanding of how to balance innovation with reliability and how to design AI applications that are not only powerful but also consistent, secure, and user-focused.
Key takeaways:- Testing LLMs requires new methods, not just old QA practices.Combine automation + human oversight for best results.
- Build feedback and safety into the system from the start.
- Quality is a continuous journey, not a release milestone.
