ByteByteAI – Learn by Doing. Become an AI Engineer

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, DeepSeek, Qwen, Gemma)
• 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

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

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)
Agent Evaluation

Reasoning and Thinking LLMs

• Overview of reasoning models like OpenAI’s “o” family and DeepSeek-R1

Inference-time Techniques

• Inference-time scaling
• CoT prompting
• Parallel sampling
• Sequential sampling
• 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)

Local Deployment

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

Ship a portfolio-ready AI project from idea to demo

• Choose: pick your own idea, or start from a curated list
• Build: implement using techniques from the course
• Iterate: get real-time feedback from the instructor as you build
• Optional Demo: present your project on final demo day

What You’ll Get