AI Engineering Topics Covered Foundation Models Model as a Service (MaaS) Scaling Laws Self-Supervised Learning Tokenization & Byte Pair Encoding (BPE) Masked Language Models Auto-Regressive Language Models Multimodal Large Language Models Embeddings Prompt Engineering Retrieval-Augmented Generation (RAG) Fine-Tuning AI Application Planning (Use Case Evaluation & Metrics) The AI Engineering Stack (Application Development, Model Development, and Infrastructure) Foundation Model Training Pipeline Training Data Modeling Post-Training Sampling LLM Evaluation Challenges in Evaluating Foundation Models Language Modeling Metrics Exact Evaluation Methodologies AI as a Judge Ranking Models with Comparative Evaluation Evaluation Pipelines Evaluation Criteria Model Selection Designing Evaluation Pipelines Prompt Engineering Introductory Prompt Engineering Concepts In-Context Learning System Prompts vs. User Prompts Context Length and Context Efficiency Prompt Engineering Best Practices Prompt Engineering Tools Organizing and Versioning Prompts Defensive Prompt Engineering Strategies Prompt Extraction, Jailbreaking, and Injection Attacks Defenses Against Prompt Attacks (Model, Prompt, and System Levels) Total Coverage: Foundation Models → Training → Post-Training → Evaluation → Prompt Engineering → RAG → Fine-Tuning → AI Application Development → AI Infrastructure. Link to free lectures : https://youtube.com/playlist?list=PLYA5eF5BJrUg submitted by /u/Negative_War_65
Originally posted by u/Negative_War_65 on r/ArtificialInteligence