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

Target Audience

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

Workshop Agenda

1. (15min) Foundations and Environment Setup

• 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

2. (45min) Building RAG-enabled Applications

• 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

3. (20min) Application Observability and Evaluation

• Implementing comprehensive LLM application monitoring
• Adding evaluation metrics and performance tracking
• Deploying guardrails for enhanced reliability
• Hands-on: run batches of evaluations

4. (10min) Integration and Next Steps

• 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

Prerequisites for Participants

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

Useful Links