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Generative AI — Free Course — Fun with Learning Technology

Home›Courses›Generative AI

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Generative AI

LLMs, embeddings, RAG, agentic AI, and GenAI engineering

41 topics8 sections41 lessons ready

01

LLM Fundamentals

5

Introduction to Generative AI and LLMs
Transformer Architecture
Tokenization and Context Windows
Temperature, Top-k, Top-p Sampling
LLM Benchmarks and Evaluation

02

Prompt Engineering

6

Prompt Engineering Fundamentals
Zero-shot and Few-shot Prompting
Chain-of-Thought (CoT) Prompting
Structured Output and JSON Mode
System Prompts and Personas
Prompt Injection and Safety

03

Embeddings and Semantic Search

5

What are Embeddings
Embedding Models — OpenAI, Sentence Transformers
Cosine Similarity and Distance Metrics
Semantic Search Implementation
Chunking Strategies for Documents

04

Vector Databases

5

Introduction to Vector Databases
ChromaDB — Setup and Usage
Pinecone — Setup and Usage
Weaviate and Qdrant Overview
FAISS for Local Vector Search

05

RAG — Retrieval Augmented Generation

5

RAG Architecture Overview
Naive RAG Implementation
Advanced RAG — Re-ranking and Hybrid Search
Evaluation of RAG Systems
RAG with LangChain

06

Fine-tuning and Model Training

6

When to Fine-tune vs RAG vs Prompt
Supervised Fine-tuning (SFT)
LoRA and QLoRA — Parameter Efficient Fine-tuning
RLHF and DPO
Hugging Face Trainer API
Model Evaluation after Fine-tuning

07

Agentic AI

6

Agentic AI — ReAct Pattern
Tool Use and Function Calling
Multi-Step Planning with LLMs
Multi-Agent Systems
LangGraph — Stateful Agents
Memory in AI Agents

08

Interview Prep

3

GenAI Interview Questions — Fundamentals
GenAI Interview Questions — RAG and Agents
GenAI System Design

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