Learn by Doing. Become an AI Engineer.

What Awaits You

Project 1. LLM Playground

• Building your own sandbox for working with LLM
• Basics of language models: tokenization, architectures (GPT, Llama), text generation methods
• Post-training: SFT, RLHF
• Quality assessment methods: metrics, benchmarks, human evaluation

Project 2. Customer Support Chatbot on RAG and Prompt Engineering

• Model adaptation practice: fine-tuning, PEFT, LoRA
• Prompt engineering techniques: few-shot, zero-shot, chain-of-thought
• Retrieval-Augmented Generation: search, indexing, generation
• Evaluation of RAG systems

Project 3. "Ask-the-Web" Agent

• Building an agent that works with tools and the web
• Agent systems: planning, reflection, multiprocess workflows
• Tool calling and multi-agent approaches
• Methods for assessing agent efficiency

Project 4. Deep Research with Search and Reasoning Models

• Working with modern reasoning-LLM (e.g., OpenAI o1, DeepSeek-R1)
• Inference methods: CoT, Tree of Thoughts, self-consistency
• Training on reasoning data: SFT, RL with verifier, self-refinement

Project 5. Multimodal Agent (Text - Image/Video)

• Generation of images and videos: diffusion, GAN, VAE
• Architectures and training of diffusion models (U-Net, DiT)
• Quality assessment methods for generation: IS, FID, CLIP
• Building end-to-end T2I and T2V systems