WHAT YOU GET?
A 6-week live, intensive program with examples, video lessons, and projects that teach you everything you need to start building with AI.
Course Outline (Project-Based Learning)
Project 1
Build an LLM Playground
LLM Overview and Foundations
Pre-Training
Data collection (manual crawling, Common Crawl)
Data cleaning (RefinedWeb, Dolma, FineWeb)
Tokenization (e.g., BPE)
Architecture (neural networks, Transformers, GPT family, Llama family)
Text generation (greedy and beam search, top-k, top-p)
Post-Training
SFT
RL and RLHF (verifiable tasks, reward models, PPO, etc.)
Evaluation
Traditional metrics
Task-specific benchmarks
Human evaluation and leaderboards
Chatbots’ Overall Design
Project 2
Build a Customer Support Chatbot using RAGs and Prompt Engineering
Overview of Adaptation Techniques
Finetuning
Parameter-efficient fine-tuning (PEFT)
Adapters and LoRA
Prompt Engineering
Few-shot and zero-shot prompting
Chain-of-thought prompting
Role-specific and user-context prompting
RAGs Overview
Retrieval
Document parsing (rule-based, AI-based) and chunking strategies
Indexing (keyword, full-text, knowledge-based, vector-based, embedding models)
Generation
Search methods (exact and approximate nearest neighbor)
Prompt engineering for RAGs
RAFT: Training technique for RAGs
Evaluation (context relevance, faithfulness, answer correctness)
RAGs’ Overall Design
Project 3
Build an “Ask-the-Web” Agent similar to Perplexity with Tool calling
Agents Overview
Agents vs. agentic systems vs. LLMs
Agency levels (e.g., workflows, multi-step agents)
Workflows
Prompt chaining
Routing
Parallelization (sectioning, voting)
Reflection
Orchestration-worker
Tools
Tool calling
Tool formatting
Tool execution
MCP
Multi-Step Agents
Planning autonomy
ReACT
Reflexion, ReWOO, etc.
Tree search for agents
Multi-Agent Systems (challenges, use-cases, A2A protocol)
Evaluation of agents
Project 4
Build “Deep Research” Capability with Web Search and Reasoning Models
Reasoning and Thinking LLMs
Overview of reasoning models like OpenAI’s “o” family and DeepSeek-R1
Inference-time Techniques
Inferece-time scaling
CoT prompting
Self-consistency
Sequential revision
Tree of Thoughts (ToT)
Search against a verifier
Training-time techniques
SFT on reasoning data (e.g., STaR)
Reinforcement learning with a verifier
Reward modeling (ORM, PRM)
Self-refinement
Internalizing search (e.g., Meta-CoT)
Project 5
Build a Multi-modal Generation Agent
Overview of Image and Video Generation
VAE
GANs
Auto-regressive models
Diffusion models
Text-to-Image (T2I)
Data preparation
Diffusion architectures (U-Net, DiT)
Diffusion training (forward process, backward process)
Diffusion sampling
Evaluation (image quality, diversity, image-text alignment, IS, FID, and CLIP score)
Text-to-Video (T2V)
Latent-diffusion modeling (LDM) and compression networks
Data preparation (filtering, standardization, video latent caching)
DiT architecture for videos
Large-scale training challenges
T2V’s overall system
Project 6
Capstone Project
Choose your own idea
Build with techniques from the course
Get real-time feedback from the instructor as you build
Demo + feedback session
Meet Your Instructor
Ali Aminian is a best-selling author of multiple books on machine learning and generative AI. With over a decade of experience at leading tech companies, he has built AI systems that are intelligent, safe, and efficient. He also contributes to AI courses at Stanford University, combining technical expertise with a passion for teaching.
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