Large Language Models (LLMs) & Prompt Engineering
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How LLMs Work Under the Hood
From Next-Token Prediction to Intelligence
Preview
Attention and the Transformer Architecture
Pretraining Instruction Tuning and RLHF
3 lessons
Advanced Prompting Techniques
Few-Shot Prompting and In-Context Learning
Chain-of-Thought and Structured Reasoning
ReAct and Retrieval-Augmented Generation Patterns
3 lessons
Fine-Tuning and Efficient Training
When to Fine-Tune and When to Prompt
LoRA and Parameter-Efficient Methods
QLoRA and Training on a Budget
3 lessons
Building AI Applications
Working with the OpenAI and Anthropic APIs
Orchestrating Workflows with LangChain
Document Pipelines with LlamaIndex
3 lessons
Embeddings and Vector Databases
What Embeddings Actually Represent
Chunking Strategies for Retrieval
Choosing and Integrating a Vector Database
3 lessons
Evaluation Safety and Reliability
Evaluating LLM Outputs Systematically
Understanding and Reducing Hallucination
Guardrails Red Teaming and Safe Deployment
3 lessons
Embeddings and Vector Databases
Choosing and Integrating a Vector Database
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