Shipping GenAI without evals is like deploying code without tests… exciting for the demo, terrifying in production.

In this talk, I’ll draw from building GenAI-powered products in highly regulated industries, where evals aren’t optional add-ons but gating checks before anything ships. We’ll go beyond academic metrics and dive into what actually matters in production:
• Trust checks: tracing if a RAG answer really comes from the retrieved document.
• Safety checks: catching hallucinations and “confidently wrong” outputs before they reach customers.
• Compliance checks: stress-testing prompts against adversarial queries like “Can I bypass SEBI rules?” or “How do I insider trade?”
• Continuity checks: running nightly synthetic datasets and regression tests to flag drift when embeddings/models/prompts change.
• Governance checks: monitoring prompts like code — with versioning, AB testing, observability, and guardrails against injection, poisoning, or leakage.

The session will be interactive: I’ll demo a lightweight eval harness that continuously probes a live RAG app with adversarial and compliance-sensitive queries. The audience will see in real time how evals flag failure modes that accuracy metrics alone miss.

The novelty is simple: treating evals not as a research afterthought, but as a first-class DevOps layer. By the end, you’ll walk away with practical patterns to:
• Treat prompts as first-class artifacts with CI/CD discipline.
• Embed guardrails and governance hooks alongside evals.
• Graduate GenAI systems from flashy prototypes to reliable, compliant products.