LLM Engineering: Master AI, Large Language Models & Agents Training

Curriculum:

Module 1: Introduction to LLM Engineering

• Overview of Generative AI and Large Language Models
• Evolution of LLMs: From GPT to Claude, Gemini, Llama, Mistral, and Falcon
• Key Concepts: Tokens, Embeddings, Context Windows, and Parameters
• Understanding Transformers, Attention Mechanisms, and Fine-Tuning
• LLM Use Cases Across Industries

Module 2: Prompt Engineering Essentials

• Foundations of Prompt Design
• Prompt Templates, Context Framing, and Role Instructions
• Chain-of-Thought (CoT) and Self-Consistency
• Multi-turn Conversations and Memory Handling
• Advanced Prompting Techniques (Few-shot, Zero-shot, Tool-use Prompts)
• Hands-on: Building Prompt Templates for Legal, Finance, and Marketing Tasks

Module 3: Working with Pre-Trained LLMs

• Accessing LLMs via OpenAI, Anthropic, Google, and Hugging Face APIs
• Managing Tokens and Rate Limits
• Comparing Proprietary vs Open-Source Models
• Model Selection Criteria for Business Use Cases
• Hands-on: Deploying a GPT-based chatbot

Module 4: Building Custom LLM Pipelines

• Overview of LangChain, LlamaIndex, and Semantic Kernel
• Document Loading, Parsing, and Chunking
• Vector Databases (Pinecone, Weaviate, FAISS, ChromaDB)
• Retrieval-Augmented Generation (RAG) Workflows
• Query Optimization and Context Injection
• Hands-on: Creating a Knowledge-Based Chatbot using RAG

Module 5: Fine-Tuning and Model Adaptation

• Fine-tuning vs. Instruction-tuning vs. Reinforcement Learning with Human Feedback (RLHF)
• Data Preparation and Labeling for Fine-tuning
• Tools: Hugging Face Trainer, LoRA, QLoRA, PEFT
• Fine-tuning Open-Source Models (Llama 3, Mistral, Falcon)
• Evaluation Metrics and Testing
• Hands-on: Fine-tuning a Domain-Specific LLM

Module 6: LLM Agents and Tool Integration

• Understanding AI Agents and Agent Architectures
• LangChain Agents, LangGraph, and Function Calling
• Planning, Reasoning, and Multi-Step Execution
• Integrating External Tools (APIs, Databases, Search Engines)
• Creating Autonomous AI Workflows
• Hands-on: Building a Research Assistant AI Agent

Module 7: Multi-Agent Systems and Orchestration

• Concepts of Multi-Agent Collaboration
• Designing Agent Networks for Complex Tasks
• Role-based Agents (Planner, Executor, Evaluator)
• Communication Protocols and Task Distribution
• Case Study: Multi-Agent System for Business Automation

Module 8: Evaluation, Optimization, and Ethics

• Measuring LLM Performance and Accuracy
• Reducing Hallucinations and Bias
• Secure Prompt Design and Data Governance
• Cost Optimization and Token Management
• Ethical and Regulatory Considerations (EU AI Act, Data Privacy)

Module 9: Deploying and Scaling LLM Applications

• Containerization (Docker) and CI/CD for LLM Applications
• API Deployment using FastAPI and Streamlit
• Scaling with Kubernetes, AWS Sagemaker, or Azure AI Studio
• Monitoring and Logging with Prometheus & LangFuse
• Hands-on: End-to-End Deployment of an LLM-based Application

Module 10: Capstone Project – Building a Domain-Specific AI Agent

• Problem Definition and Use Case Selection (Legal, Finance, HR, or Healthcare)
• Data Ingestion, RAG Setup, and Prompt Optimization
• Building Agent Workflow with LangChain/LangGraph
• Testing, Optimization, and Deployment
• Project Presentation & Certification