ByteByteAI – Learn by Doing Become an AI Engineer

Learn by Doing.
Become an AI Engineer.

Taught by Best-Selling Author Ali Aminian

Meet Your Instructor

Ali Aminian

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.

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

Is this course for you?

If you want to start learning Al from scratch,

this is for you!

If you’ve learned some concepts but still feel confused,

this is for you!

If you want to build a few neural
network models and agents quickly,

this is for you!

If you are tired of learning
Al alone,

this is for you!

Course Highlights

1Structured, Systematic Learning Path

2Intuitive, Visual Explanations

3Project-Based Learning That Sticks

4Beginner-Friendly Code that You can Run

5Learn the ‘Why’ Behind the ‘How’

What You’ll Get