Template-Driven Prompt Engineering for Streaming Financial Events
summary
Claims that template libraries plus versioned prompts and policy rules turn high throughput financial streams into reliable, auditable automations. Uses a Microsoft-style semantic kernel or agentic framework to route events to plugins, inject schema and operational context from a vector database, run LLMs (Gemini or similar), validate outputs with an output validator, and emit actionable steps like replaying orders or deleting duplicates. Shows reduced latency, measurable audit trails, and a feedback pipeline for model and vector updates so templates stay current.
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chapters (8)
- 0:00Introduction to Real-Time FinOps Intelligence
- 0:55Challenges in Financial Platforms
- 1:55AI and Observability in Financial Systems
- 4:58Importance of Context in AI Prompting
- 9:26Template-Driven Prompt Engineering
- 12:50Operational Knowledge and Policy Constraints
- 14:50Implementing AI in Financial Event Streams
- 22:32Key Takeaways and Conclusion
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