LLM Engineering: Master AI, Large Language Models & Agents Course Syllabus

Overview: This comprehensive course is designed for developers and engineers looking to master the essentials of LLM engineering and build production-ready AI applications. You'll progress from foundational concepts to advanced implementations, covering prompt engineering, vector storage, RAG systems, and AI agents. With approximately 7 hours of total content, the course combines theory with hands-on practice using industry-standard tools like OpenAI, LangChain, Pinecone, and AutoGen. Each module builds toward a final capstone project, ensuring you gain real-world experience in deploying LLM-powered applications.

Module 1: Introduction to LLM Engineering

Estimated time: 0.5 hours

• What is LLM engineering and why it matters today
• Overview of key large language models: GPT, Claude, LLaMA, Mistral
• Basic architecture of transformer-based LLMs
• Roles and responsibilities of an LLM engineer

Module 2: Prompt Engineering & APIs

Estimated time: 1 hours

• Understanding zero-shot, few-shot, and chain-of-thought prompting
• Calling LLMs via OpenAI and Anthropic APIs
• Designing effective prompts for accuracy and consistency
• Optimizing API usage for cost and performance

Module 3: Embeddings, Vectors & Memory

Estimated time: 1 hours

• How embeddings work and their role in semantic search
• Use cases for embeddings in personalization and retrieval
• Introduction to vector databases: Pinecone and FAISS
• Storing and querying vector embeddings for memory

Module 4: Retrieval-Augmented Generation (RAG)

Estimated time: 1.25 hours

• Understanding RAG architecture and its benefits
• Connecting LLMs with custom data sources
• Implementing retrieval using embedding search
• Building context-aware question-answering systems

Module 5: Agents & LangChain Frameworks

Estimated time: 1.25 hours

• What are LLM agents and how they function
• Building dynamic workflows with LangChain
• Using AutoGen for multi-agent collaboration
• Orchestrating complex tasks with agent frameworks

Module 6: Evaluation & Safety in LLMs

Estimated time: 0.75 hours

• Evaluating LLM outputs for hallucinations and factual accuracy
• Detecting and reducing bias in model responses
• Implementing safety measures for responsible deployment
• Best practices for monitoring and auditing LLM behavior

Module 7: Real-World Projects & Capstone

Estimated time: 1 hours

• End-to-end build of an AI application using LangChain, RAG, and Pinecone
• Deploying a document Q&A system with real-time retrieval
• Creating a chatbot with persistent memory and custom knowledge

Prerequisites

• Intermediate proficiency in Python programming
• Familiarity with APIs and RESTful services
• Basic understanding of machine learning concepts

What You'll Be Able to Do After

• Design and implement effective prompts for various LLM tasks
• Integrate vector databases with LLMs for enhanced retrieval
• Build and deploy Retrieval-Augmented Generation systems
• Create intelligent agents using LangChain and AutoGen
• Develop, evaluate, and safely deploy production-grade LLM applications