Modern financial platforms ingest and act on high-velocity event streams across cloud-native architectures, payments, ledger updates, fraud signals, identity events, where milliseconds matter and auditability is non-negotiable. This talk presents a template-driven prompt engineering framework that enables Large Language Models (LLMs) to interpret real-time financial events with higher semantic accuracy and more reliable, structured outputs than ad-hoc prompting, while maintaining strong controls for security and compliance.

We introduce an end-to-end production architecture that couples distributed streaming with ACID-compliant storage and change-data feeds, and enriches LLM reasoning using policy-governed context injection (schema metadata, operational telemetry, and time-bounded transaction history). The framework tackles three recurring production challenges: dynamic context that evolves every second, persona-specific prompt templates for engineering, operations, and compliance teams, and deterministic output schemas that downstream automation can validate and execute safely.

You’ll see how modular templates embed data contracts, guardrails, and evaluation hooks to reduce hallucination risk, prevent sensitive data leakage, and produce traceable decisions. Real-world outcomes include automated incident triage, anomaly detection with natural-language explanations, and schema drift detection with recovery playbooks, along with regulatory-ready audit trails that preserve end-to-end provenance.

Attendees will leave with practical template patterns, rollout strategies, and evaluation methods to integrate AI-powered operational intelligence into financial event processing, without compromising security posture, reliability, or compliance.