Overview
Syllabus
Introduction - 0:00
1 LLM - 0:14
2 Prompt - 1:05
3 Prompt Engineering - 1:39
4 Few-shot Prompting - 2:36
5 Context Window- 3:03
6 Token - 4:10
7 Inference - 5:42
8 Parameter - 6:27
9 Temperature - 7:08
10 Prompt Injection - 7:52
11 Guardrails - 9:23
12 Hallucination - 9:55
13 RAG - 10:16
14 Semantic Search - 10:57
15 Embeddings - 11:43
16 Chunk - 12:25
17 Vector Database - 13:11
18 AI Agents - 13:50
19 Agentic AI - 14:10
20 Function Calling - 15:06
21 MCP - 15:48
22 Fine-tuning - 16:33
23 Distillation - 17:30
24 Reinforcement Learning - 17:52
25 RLHF - 18:28
26 Reasoning Models - 18:57
27 Test-time Compute - 19:40
28 Train-time Compute - 20:37
29 Pre-training - 21:16
30 Post-training - 21:39
Taught by
Shaw Talebi