LLM Engineering Training Course

The LLM Engineering Training Course from The Hub of Knowledge is designed for professionals who want to build real-world AI applications using Large Language Models (LLMs). This comprehensive course covers prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, vector databases, fine-tuning, API integrations, deployment strategies, and enterprise AI architecture.

Participants will gain practical experience with leading AI platforms and frameworks including OpenAI, LangChain, Hugging Face, LlamaIndex, and modern AI orchestration tools used across industries.

The LLM Engineering Course equips learners with the skills required to design, develop, deploy, and optimize AI-powered solutions using modern Large Language Models. The course combines theoretical concepts with practical projects and enterprise use cases.

This training focuses on:

• Generative AI fundamentals
• Prompt Engineering techniques
• LLM architectures
• Retrieval-Augmented Generation (RAG)
• AI agents and autonomous workflows
• Vector databases and embeddings
• Fine-tuning LLMs
• AI model deployment
• LLMOps and monitoring
• Enterprise AI governance and security

Module 1: Introduction to Generative AI & LLMs

• Understanding AI, ML, Deep Learning & Generative AI
• Evolution of Large Language Models
• Transformer Architecture Basics
• Understanding Tokens, Embeddings & Attention
• Popular LLMs Overview

Module 2: Prompt Engineering Fundamentals

• Zero-shot & Few-shot prompting
• Chain-of-thought prompting
• Role prompting techniques
• Prompt optimization strategies
• Structured output generation

Module 3: OpenAI & API Integrations

• Working with OpenAI APIs
• API authentication & usage
• Building chatbot applications
• Text summarization & classification
• AI content generation workflows

Module 4: LangChain & LLM Frameworks

• Introduction to LangChain
• Chains & Agents
• Memory management
• Tool integrations
• Multi-step AI workflows

Module 5: Retrieval-Augmented Generation (RAG)

• Understanding RAG architecture
• Document ingestion pipelines
• Chunking strategies
• Embeddings generation
• Context-aware AI responses

Module 6: Vector Databases

• Introduction to Vector Databases
• Pinecone, Weaviate & FAISS
• Similarity search
• Semantic search implementation
• Enterprise search applications

Module 7: Fine-Tuning & Custom Models

• Fine-tuning concepts
• Dataset preparation
• Instruction tuning
• Parameter-efficient fine-tuning
• Evaluating model performance

Module 8: AI Agents & Automation

• Autonomous AI agents
• Multi-agent systems
• Workflow automation
• AI orchestration
• Business AI applications

Module 9: LLM Deployment & MLOps

• Deploying LLM applications
• Cloud deployment strategies
• Docker & API deployment
• Monitoring AI systems
• Scaling enterprise AI solutions

Module 10: Security, Ethics & Governance

• Responsible AI principles
• AI security considerations
• Data privacy & compliance
• Bias mitigation
• Enterprise governance frameworks

Capstone Project

• Build a production-ready AI assistant
• Create a RAG-based chatbot
• Deploy enterprise AI applications
• End-to-end LLM Engineering implementation