Core AI Engineering: A Complete Guide
A comprehensive curriculum covering the full AI engineer stack — from LLM products and multi-modal chatbots to HuggingFace, RAG, fine-tuning, and autonomous multi-agent systems. Each chapter includes explanations, code examples, diagrams, and hands-on exercises.
Table of Contents
- 1. Build Your First LLM Product Ollama, LLM types, transformers, business apps, streaming
- 2. Multi-Modal Chatbot LangChain, Gradio, tool calling, DALL-E, TTS, SQLite agents
- 3. Open-Source Gen AI with HuggingFace Pipelines, tokenizers, transformers, quantization, Whisper, synthetic data
- 4. LLM Showdown Scaling laws, benchmarks, code gen, model selection, eval metrics
- 5. Mastering RAG Vector embeddings, LangChain, ChromaDB, evaluations, advanced RAG
- 6. ML to DL to Fine-Tuning XGBoost, PyTorch, SFT, frontier model fine-tuning
- 7. Fine-Tuned Open-Source Models QLoRA, adapters, TRL, W&B, inference evaluation
- 8. Autonomous Multi-Agent Systems Structured outputs, planning agents, Modal, deal scanner
- 9. Practice & Resources Quick guide, study plan, bootcamp, certificate path
- 10. References Glossary, cheatsheet, API reference