Practical AI Summit 2025

Leverage AI without Fear

AI is no longer a futuristic technology—it is rapidly becoming a core building block of modern software systems. Yet many software professionals hesitate to adopt AI in production, not because of hype fatigue, but because of very real engineering concerns.

Will AI break my existing system?

Will AI create security or compliance risks?

Will AI reduce my role or replace parts of my job?

How do I trust an LLM’s output inside production code?

Make AI your Friend

If you’re unsure how to confidently address these questions, this hands-on workshop is for you.

This workshop is designed to remove the fear and replace it with engineering clarity. You’ll learn how to integrate AI into your existing codebase using safe architectural patterns, clear system boundaries, validation and testing strategies, and responsible model oversight.

Rather than replacing deterministic systems, you’ll learn how to augment them intelligently, ensuring reliability, observability, and control.

By the end of the workshop, you will have:

Deployed AI into your current codebase a real workflow
Understood how to evaluate and trust AI outputs
Learned how to scale AI responsibly across your organization

2 Hands On Workshop 💎

LEARN BY DOING

Building Production-Grade RAG Applications

Learn how to design, evaluate, and deploy Retrieval-Augmented Generation systems that actually work in production. Covers architecture patterns, common failure modes, and best practices for accuracy, latency, and cost control.

Deploying Responsible AI Inside Your Current Codebase

Understand how to integrate AI safely into existing applications without rewriting your system. Learn practical techniques for guardrails, monitoring, and compliance while maintaining reliability and trust.

From Prompts to Systems: Designing Reliable AI Workflows

Move beyond prompt experimentation to structured, multi-step AI workflows. Learn how to design AI systems that behave predictably, handle errors gracefully, and fit real software engineering standards.

Evaluating & Testing AI in Production

Traditional testing breaks with AI. This session covers practical evaluation strategies, regression testing for AI outputs, and monitoring techniques to detect drift, degradation, and unexpected behavior in live systems.

Integrating AI into Real-World Software Architecture

Learn where AI belongs (and doesn’t) in modern software systems. Covers architectural patterns, API design, async workflows, and how to introduce AI incrementally without breaking performance or maintainability.