Workshop

The workshop combines architectural discussions with hands-on implementation. Developers will: - Write code for each major component of an LLM application - Debug common failure patterns in RAG implementations - Deploy monitoring solutions for tracking application health - Optimize response quality through systematic evaluation - Implement security best practices for production deployment

Workshop Agenda

Foundations and Environment Setup (15 minutes) - Understanding LLM architectures and serverless deployment patterns - Setting up the development environment and required dependencies - Introduction to core APIs and development patterns - Overview of production-grade fine-tuning approaches

Building RAG-enabled Applications (45 minutes) - Implementing vector search for efficient context retrieval - Developing interactive chat interfaces with Python web frameworks - Building end-to-end RAG pipelines with proper error handling - Hands-on: Creating a real-time contextual chatbot

Application Observability and Evaluation (20 minutes) - Implementing comprehensive LLM application monitoring - Adding evaluation metrics and performance tracking - Deploying guardrails for enhanced reliability - Hands-on: run batches of evaluations

Integration and Next Steps (10 minutes) - Best practices for production deployment - Common pitfalls and debugging strategies - Scaling considerations and optimization techniques - Open discussion and resource sharing

Key Takeaways Design and implement RAG architectures with hybrid search for enhanced context handling Build responsive chat interfaces using modern Python frameworks Deploy production-ready observability for LLM applications Implement guardrails and evaluation metrics for application reliability Debug and optimize LLM application performance at scale

Target Audience

Software engineers and ML practitioners who want to move beyond basic LLM integrations to building production-grade applications.

Prerequisites

• Python development experience
• Basic understanding of ML/LLMs
• Familiarity with web frameworks
• Experience with API integrations
• Docker Desktop or other container tools