AI-Native TrainingModule 6
Learning Objectives
Build production-ready GenAI applications
Implement Retrieval-Augmented Generation (RAG)
Fine-tune models for specific domains
Optimize for cost and performance
Topics Covered
1
RAG (Retrieval-Augmented Generation)
Complete RAG architecture and implementation
Document ingestion and chunking strategies
Embedding generation and vector databases
Semantic search and hybrid retrieval
Advanced techniques: multi-query, HyDE, re-ranking
2
Fine-Tuning vs RAG
When to use each approach
Use case analysis: when to use each
Fine-tuning process and best practices
Cost-benefit analysis
Hybrid approaches
3
Production GenAI Applications
Architecture, UX, and security
API layer design and rate limiting
Streaming responses and caching
Prompt injection prevention
PII detection and compliance
Hands-On Projects
Production RAG System
advanced5 hours
Build production-ready RAG application
Fine-Tuned Model
advanced4 hours
Implement fine-tuned model for specific domain
Secure GenAI API
advanced3 hours
Create secure GenAI API with authentication