Large Language Models (LLMs) & Prompt Engineering
0 of 18 lessons complete (0%)
Exit Course
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
What Embeddings Actually Represent
You don’t have access to this lesson
Please register or sign in to access the course content.
Take course
Sign in
Previous
Next