Welcome to LLM Mastery 2025
🎯 What You'll Learn
- Build production-ready LLM applications from scratch
- Master advanced techniques: RAG, fine-tuning, agents, multimodal LLMs
- Implement security, monitoring, and cost optimization
- Handle challenges: hallucinations, adversarial attacks, privacy
- Understand cutting-edge research: MoE, Mamba, Constitutional AI
- Build transformers from scratch for deep technical understanding
📋 Prerequisites
No prior LLM experience required! You should have:
- Basic Python programming knowledge
- Familiarity with machine learning concepts (helpful but not required)
- Enthusiasm to learn and build real-world applications
🚀 Course Philosophy
This course is designed with a production-first approach. Every lesson includes practical implementation, security considerations, and real-world best practices. You won't just learn theory—you'll build a complete portfolio of production-ready applications.
Learning Methodology
Theory
Deep conceptual understanding with clear explanations and visual aids
Hands-On Labs
2-5 hour practical labs for each concept with step-by-step guidance
Projects
Build complete applications that you can showcase in your portfolio
Code Review
Learn best practices through comprehensive code examples
⚡ ETMI5: Explain To Me In 5
Each week starts with a 5-minute quick overview (ETMI5) that gives you the essential concepts before diving deep. Perfect for quick reference or refreshing your memory.
LLM Foundations & Architecture
📚 Topics Covered
Understanding self-attention, positional encoding, and layer normalization
BPE, WordPiece, SentencePiece and vocabulary construction
Fine-tuning strategies for specific domains and tasks
Pre-training, supervised fine-tuning, and alignment techniques
🔬 Hands-On Lab 1.1
2 hoursBuilding Your First LLM Application
Set up development environment, use OpenAI API, and build a simple chatbot with context management.
- API authentication and rate limiting
- Prompt construction and parameter tuning
- Response parsing and error handling
📚 Week 1 Project
BeginnerInteractive Document Q&A System
Build a complete question-answering system that can analyze documents and provide intelligent responses with source attribution.
Advanced Prompt Engineering
⚡ ETMI5 Summary
Master advanced prompting techniques to dramatically improve LLM performance. Learn Zero-shot, Few-shot, Chain-of-Thought (CoT), Tree-of-Thoughts (ToT), ReACT, and self-consistency methods that enable complex reasoning.
Key Techniques
- Zero-Shot Learning: Leverage pre-trained knowledge without examples
- Few-Shot Learning: Provide examples to guide model behavior
- Chain-of-Thought (CoT): Enable step-by-step reasoning
- Tree-of-Thoughts (ToT): Explore multiple reasoning paths
- ReACT: Combine reasoning with action-taking
Fine-Tuning & Parameter-Efficient Methods
⚡ ETMI5 Summary
Master fine-tuning techniques to adapt pre-trained LLMs to specific tasks and domains. Learn LoRA, QLoRA, RLHF, and DPO for efficient model adaptation without training billions of parameters.
Key Techniques
- LoRA (Low-Rank Adaptation): Train only 0.1-10% of parameters
- QLoRA: 4-bit quantization for fine-tuning 70B models on single GPU
- RLHF: Align models with human preferences using reinforcement learning
- DPO: Direct preference optimization without reward models
Retrieval Augmented Generation (RAG)
⚡ ETMI5 Summary
Master RAG architecture to ground LLM responses in external knowledge. Build production systems with vector databases, embeddings, and advanced retrieval strategies.
RAG Pipeline
- Ingestion: Document loading, chunking, embedding, and indexing
- Retrieval: Semantic search, reranking, and hybrid retrieval
- Generation: Context-aware responses with source citations
- Vector DBs: Pinecone, Weaviate, ChromaDB, FAISS
LLM Tools, Function Calling & Agents
⚡ ETMI5 Summary
Extend LLM capabilities with tools and function calling. Build autonomous agents that can take actions, use APIs, and solve complex multi-step tasks.
LLM Evaluation & Benchmarking
⚡ ETMI5 Summary
Master evaluation techniques for LLM performance. Learn metrics, benchmarks, and best practices for assessing model quality and reliability.
Production Deployment & Serving
⚡ ETMI5 Summary
Deploy LLMs to production with optimal performance, scalability, and cost-efficiency. Learn serving frameworks (vLLM, TGI), quantization, batching, and optimization techniques.
LLMOps & Monitoring
⚡ ETMI5 Summary
Implement MLOps practices for LLMs. Learn monitoring, logging, versioning, A/B testing, and continuous improvement strategies for production systems.
LLM Challenges & Limitations
⚡ ETMI5 Summary
Understand and address LLM limitations including hallucinations, biases, prompt injection, jailbreaks, and security vulnerabilities. Build robust, safe systems.
Research Trends & Future Directions
⚡ ETMI5 Summary
Explore cutting-edge LLM research including multimodal models (GPT-4V, CLIP), mixture of experts, state space models (Mamba), and emerging architectures.
Technical Deep Dive & Building from Scratch
⚡ ETMI5 Summary
Build transformer models from scratch. Understand every component deeply by implementing attention, embeddings, training loops, and optimizations in pure PyTorch.
🏆 Capstone Project
Project Options
Option A: Enterprise Knowledge Assistant
Build a production RAG system with multimodal support, advanced search, and compliance features.
Option B: Domain-Specific Expert System
Fine-tune and deploy a specialized LLM for medical, legal, or scientific applications.
Option C: Multi-Agent Research Platform
Create a system with multiple specialized agents collaborating on complex tasks.
Required Deliverables
Clean, documented, production-ready code
Architecture, API docs, user guide
8-12 page technical paper
5-10 minute presentation
Comprehensive testing (80%+ coverage)
Production deployment guide
📥 Download Complete Course Materials
Get the full course markdown document with all code examples, labs, and projects.
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