As large language models move from experimentation to production, the real challenge is no longer capability – it’s trust. In this session, we’ll share a practical framework for building LLM-powered features that are reliable, measurable, and safe from day one. Attendees will learn how Attest designed responsible AI products through clear ethics principles, embedded guardrails, and iterative development cycles that combine experimentation, validation, and controlled rollout. We’ll explore how human-in-the-loop systems and structured evaluation frameworks help teams measure usefulness, safety, and real business impact.
But deployment is only the beginning. We’ll walk through how we implemented continuous evaluation and monitoring in production, including automated scoring approaches (such as G-Eval evaluations), hallucination monitoring, and fallback logic to mitigate failure modes. Participants will hear about concrete strategies for building feedback loops that continuously improve prompts, policies, and models over time.
Finally, we’ll demonstrate how these principles apply in a high-stakes, real-world use case – survey data quality assurance. We’ll show how combining rule-based systems with LLM-driven semantic validation can detect bots, inattentive respondents, and inconsistent open-text answers – with explainability built in. Attendees will leave with actionable methods for delivering cleaner datasets, more trustworthy insights, and AI systems the users can confidently rely on.
Technical Level of Session: Technical practitioner
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