English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 175 Lessons (25h 8m) | 6.37 GB
Stop memorizing random prompts. Instead, learn how Large Language Models (LLMs) actually work and how to use them effectively. This course will take you from beginner to mastering LLMs by teaching you how to create your own AI tools that will take your career to the next level.
Learn how to work with LLMs and AI. We guarantee you that this is the most comprehensive and up-to-date prompt engineering bootcamp course. You’re going to learn the skills needed to be in the top 10% of using AI in the real world.
WHAT YOU’LL LEARN
- Learn the fundamentals of prompting and its practical applications, including real-world examples from NASA
- Gain practical experience and a deeper understanding of how LLMs work (and how they don’t) through hands-on exercises
- Learn to use leading closed-source LLMs like GPT and Claude and even set up your own open-source LLMs
- Master empirically-proven prompting techniques to improve the effectiveness and utility of your interactions with LLMs
- Apply your skills in real-world scenarios through numerous guided and unguided projects that teach you to apply your skills
- Stay updated with the latest advancements in AI and prompt engineering, with continuous course updates to ensure you are always at the cutting edge
Table of Contents
1 Learn To Work With LLMs with Scott Kerr
2 Course Introduction
3 What is Prompt Engineering?
4 Why is Prompt Engineering Even a Thing?
5 Breaking GPT
6 Applied Prompt Engineering
7 Applied Prompt Engineering with NASA
8 Why is Prompt Engineering Important to You?
9 What I’m Using – Part 1
10 What I’m Using – Part 2 (OpenAI Playground)
11 Multi-Modality and Tools in LLMs
12 Getting Started with ChatGPT
13 The Basics of ChatGPT
14 ChatGPT App
15 Optional: ChatGPT Plus
16 Getting Started with Claude
17 Project Introduction
18 Setting Up Your Replit Account
19 Important Point #1 – Fundamental Truths About LLMs
20 Important Point #2 – Models are Non-Deterministic
21 The First Try
22 Building Our Snake Game – Part 1
23 Building Our Snake Game – Part 2
24 Introduction to LLMs
25 Tokens
26 Word Guessing Machines?
27 Thinking Like LLMs – Roll a Dice
28 Inside LLMs
29 The Transformer Model
30 Exercise: Visualize the LLM Architecture
31 The Training Process
32 Base Model vs. Assistant Model
33 Thinking Like LLMs – The Reversal Curse
34 Artificial General Intelligence (AGI)
35 Exercise: Use ChatGPT to Read the Research
36 The World of LLMs
37 Overview of Our Prompting Framework
38 The Standard Prompt
39 Exercise: Get Hyped to Learn!
40 The Setup
41 The System Message – Part 1
42 The System Message – Part 2
43 Exercise: Strengthen Your System Message
44 Context – What it Is
45 Context – The Context Window
46 Context – Lost in the Middle
47 Personas and Roles
48 Personas – Tone, Style and Voice
49 Custom Instructions
50 Thinking Like LLMs – Can GPT Keep a Secret?
51 Exercise: Get ChatGPT to Spill the Tea – Part 1
52 Exercise: Get ChatGPT to Spill the Tea – Part 2
53 Exercise: Get ChatGPT to Spill the Tea – Part 3
54 The User Message
55 Be Clear and Specific
56 Delimiters
57 Exercise: Identify Delimiters
58 X-Y Problem
59 In-Context Training
60 Zero Shot Prompting
61 One Shot and Few Shot Prompting
62 Language Models Are Few-Shot Learners
63 How Many Examples?
64 Thinking Like LLMs – But Wait…
65 Chain-of-Thought Prompting – Part 1
66 Chain-of-Thought Prompting – Part 2
67 Zero Shot CoT
68 Exercise: Imposter Syndrome
69 Career Coach Ideation
70 The Setup – Persona
71 The Setup – Context and Commands
72 The Instructions – Modes
73 The Instructions – Chain of Thought
74 The Instructions – Gamify
75 Meet Your Career Coach
76 The Output
77 Length
78 Formats
79 Exercise: Output an Excel File
80 Exercise: Make a Flowchart
81 Introduction to Guardrails and Jailbreaking
82 Jailbreak! (The DAN Prompt)
83 Many Shot Jailbreaking
84 Prompt Injections – Part 1
85 Prompt Injections – Part 2
86 Thinking Like LLMs – Multi-Modal Injection
87 Leaking – Part 1 (Prompt Leaking)
88 Leaking – Part 2 (Data Leaking)
89 Exposure
90 Poisoning
91 Toxicity
92 Hallucinations
93 Thinking Like LLMs – Big vs Small
94 Challenge: Conduct Your Own Mechanistic Interpretability Research on Hallucinations
95 The Model Card
96 Model Cards Deep Dive
97 Introduction to Hyperparameters and the OpenAI Playground
98 Temperature
99 Top P
100 Frequency and Presence Penalties
101 Stop Sequences
102 What Are Open Source LLMs and Why Are They Important?
103 Chatbot Arena Leaderboard
104 Battle of the Intelligence Tests
105 Exercise: Create Your Own Test Prompt (Totem)
106 Introduction to LMStudio
107 Setting Up Your Own Model – Part 1
108 Setting Up Your Own Model – Part 2
109 Introduction
110 Auto-Priming
111 Chain of Density Prompting
112 Prompt Variables
113 Prompt Chaining
114 Prompt Chaining – Programmatic Visualization
115 Exercise: Prompt Chaining – Customer Support
116 Thinking Like LLMs – Dark Magic
117 XML Tags – There’s More Than Meets The Eye!
118 Emotional Stimuli – Part 1
119 Emotional Stimuli – Part 2
120 Emotional Stimuli – Part 3 (But Why?)
121 Self-Consistency
122 ReAct Prompting
123 ReAct + CoT-SC
124 Applied Prompt Engineering with CRISPR – Part 1
125 Applied Prompt Engineering with CRISPR – Part 2
126 Tree of Thoughts – Part 1
127 Tree of Thoughts – Part 2
128 Tree of Thoughts – Part 3 (ToT via Code)
129 Tree of Thoughts – Part 4 (ToT via Chaining)
130 Tree of Thoughts – Part 5 (“Zero Shot” ToT)
131 Introduction to Prompt Testing
132 The Importance of Prompt Testing
133 Thinking Like LLMs – Schrodinger’s Prompt
134 Building a Prompt Test
135 The Golden Answer
136 Model Benchmarks
137 Deep Dive: MMLU Benchmark
138 Prompt Tests vs. Model Benchmarks?
139 The Latest: MMLU Pro
140 Evaluating Results – Human Judge
141 Evaluating Results – Code Judge
142 Evaluating Results – AI Judge
143 Deep Dive: LLMs as a Judge – Biases
144 Deep Dive: LLMs as a Judge – Prompts
145 Thinking Like LLMs – Ex Post Facto Reasoning
146 Here. We. Go!
147 Introduction to PromptFoo
148 Our Prompt Testing Framework
149 Our 1st Prompt Test (Adding Prompts, Metrics + Human Judge) – The Setup
150 Our 1st Prompt Test (Adding Prompts, Metrics + Human Judge) – The Results
151 Our 2nd Prompt Test (Adding New Prompts + Code Judge) – The Setup
152 Our 2nd Prompt Test (Adding New Prompts + Code Judge) – The Results
153 Our 3rd Prompt Test (Adding 152 Test Cases + AI Judge) – The Setup
154 Our 3rd Prompt Test (Adding 152 Test Cases + AI Judge) – The Results
155 Our 4th Prompt Test (Adding Multiple Models) – The Setup
156 Our 4th Prompt Test (Adding Multiple Models) – The Results
157 Our 5th Prompt Test (Adding System Messages) – The Setup
158 Our 5th Prompt Test (Adding System Messages) – The Results
159 Do You Realize What Just Happened?
160 Introduction – Through The Looking Glass…
161 Applied Prompt Engineering with The Turing Test
162 Mechanistic Interpretability – Part 1
163 Mechanistic Interpretability – Part 2
164 Scaling Laws – Model Size
165 Scaling Laws – Dataset Size
166 Scaling Laws – Training Compute
167 Thank You!
168 Setup Demo: Getting Your API Key
169 Setup Demo: Downloading AutoGPT
170 Setup Demo: Installing Docker
171 Launching Your Autonomous Agent – Part 1
172 Launching Your Autonomous Agent – Part 2
173 It’s Aliiiiiive! Running Your Autonomous Agent
174 Task 1: Hello World – Your First Website
175 Task 2: Python Program – Palindrome Checker
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