Generative AI & LLM Engineering in Production

Course Overview

Move beyond simple API prompts and learn how to design, evaluate, and deploy enterprise-grade LLM applications. This course focuses on practical implementation details, covering vector stores, retrieval architectures, context window optimization, and local model execution.

What You Will Learn

• Construct robust Retrieval-Augmented Generation (RAG) pipelines for enterprise search.
• Index and query unstructured documents using modern vector databases.
• Fine-tune open-weight models for domain-specific tasks using LoRA.
• Set up monitoring and evaluation frameworks to prevent model hallucination.

Syllabus Breakdown

• Module 1: Vector Storage & Embeddings: Chunking strategies, embedding models, and configuring vector indices.
• Module 2: RAG Pipeline Design: Connecting data sources, query rewrites, and re-ranking search results.
• Module 3: Model Fine-Tuning: Parameter-efficient fine-tuning techniques on custom domain datasets.
• Module 4: Application Deployment & Guardrails: Latency optimization, prompt injection security, and cost control.

Who This Is For

Engineers and developers looking to add production-ready generative AI capabilities to existing software architectures.