AI Engineering in 75 Minutes - Foundation Models, Evaluation, RAG, Agents, Finetuning & Inference

⏱️ CHAPTERS & TIMESTAMPS 0:00 — Chapter 1: Introduction to Building AI Applications with Foundation Models The rise of AI engineering, where foundation models fit, common use cases, and the full engineering stack. 7:28 — Chapter 2: Understanding Foundation Models A quick look under the hood: training data, architecture, post-training, and how text is sampled at inference. 14:30 — Chapter 3: Evaluation Methodology Why evaluating models is hard — language modeling metrics, exact scoring, AI-as-a-judge, and comparative ranking. 22:15 — Chapter 4: Evaluate AI Systems From methodology to practice: evaluation criteria, evidence-based model selection, and designing an eval pipeline. 30:21 — Chapter 5: Prompt Engineering Core prompting principles, best practices for reliability, and defending against prompt injection and attacks. 37:16 — Chapter 6: RAG and Agents Extending models beyond their weights — retrieval-augmented generation, agents that plan and use tools, and memory. 44:30 — Chapter 7: Finetuning When finetuning is worth it, the memory bottlenecks that govern it, and how the main techniques differ. 52:04 — Chapter 8: Dataset Engineering The data that powers finetuning and eval — curation, augmentation, synthesis, and processing. 59:10 — Chapter 9: Inference Optimization What drives inference cost and latency, key optimization techniques, and the speed/cost/quality trade-offs. 1:06:25 — Chapter 10: AI Engineering Architecture & User Feedback Putting it together into a production architecture and building feedback loops that keep the system improving. ✅ WHAT YOU'LL WALK AWAY WITH • A mental map of the full stack — from a raw foundation model to a deployed application • The core vocabulary: training/post-training/sampling, evaluation, RAG, agents, finetuning, inference optimization • A feel for the key decisions — prompt vs. RAG vs. finetune, and how to evaluate with evidence • Enough orientation to know exactly which topics to go deep on next 👥 WHO THIS IS FOR Software engineers, ML practitioners, and technical product builders who want to grasp the whole AI engineering landscape quickly before drilling into specifics. #AIEngineering #FoundationModels #LLM #RAG #AIAgents #MachineLearning #PromptEngineering #Finetuning #MLOps #GenerativeAI