CF

Prompt Engineering Bootcamp (Working With LLMs): Zero to Mastery

31h 45m 3s
English
Paid

Unlock your potential by understanding how Large Language Models (LLMs) truly function. Stop memorizing random prompts and start mastering the art of effective utilization. This course will guide you from a beginner level to mastering LLMs, teaching you how to create your own AI tools to elevate your career to new heights.

Why Take This Course?

Discover the unparalleled opportunity to work with LLMs and AI. We assure you that this prompt engineering bootcamp is the most comprehensive and up-to-date course available. Gain the skills required to be among the top 10% of AI users in real-world applications.

Course Overview

This intensive bootcamp will provide you with cutting-edge knowledge and practical skills. You will learn how to harness the power of AI and LLMs effectively, equipping you for success in a rapidly evolving tech landscape.

About the Author: Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

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#1: Learn To Work With AI & LLMs with Scott Kerr
All Course Lessons (218)
#Lesson TitleDurationAccess
1
Learn To Work With AI & LLMs with Scott Kerr Demo
01:01
2
Course Introduction
06:20
3
What is Prompt Engineering?
06:25
4
Why is Prompt Engineering Even a Thing?
08:32
5
Breaking GPT
04:03
6
Applied Prompt Engineering
04:25
7
Applied Prompt Engineering with NASA
09:30
8
Why is Prompt Engineering Important to You?
04:07
9
What I'm Using - Part 1
01:22
10
What I'm Using - Part 2 (OpenAI Playground)
04:03
11
Multi-Modality and Tools in LLMs
11:42
12
Getting Started with ChatGPT
01:40
13
The Basics of ChatGPT
04:09
14
ChatGPT App
01:12
15
Optional: ChatGPT Plus
01:56
16
Getting Started with Claude
04:28
17
Project Introduction
02:14
18
Important Point #1 - Fundamental Truths About LLMs
02:59
19
Important Point #2 - Models are Non-Deterministic
04:04
20
Setting Up Your Replit Account
04:45
21
The First Try
04:42
22
Building Our Snake Game - Part 1
06:13
23
Building Our Snake Game - Part 2
05:50
24
Introduction to LLMs
05:45
25
Tokens
05:18
26
Word Guessing Machines?
05:51
27
Thinking Like LLMs - Roll a Dice
06:57
28
Inside LLMs
22:14
29
The Transformer Model
07:39
30
Exercise: Visualize the LLM Architecture
03:57
31
The Training Process
09:58
32
Base Model vs. Assistant Model
07:07
33
Thinking Like LLMs - The Reversal Curse
05:15
34
Artificial General Intelligence (AGI)
05:29
35
Exercise: Use ChatGPT to Read the Research
04:48
36
The World of LLMs
06:06
37
Overview of Our Prompting Framework
03:31
38
The Standard Prompt
04:34
39
Exercise: Get Hyped to Learn!
04:05
40
The Setup
01:02
41
The System Message - Part 1
06:08
42
The System Message - Part 2
05:44
43
Exercise: Strengthen Your System Message
06:43
44
Context - What it Is
05:18
45
Context - The Context Window
09:22
46
Context - Lost in the Middle
11:27
47
Personas and Roles
08:43
48
Personas - Tone, Style and Voice
11:50
49
Custom Instructions
11:15
50
Thinking Like LLMs - Can GPT Keep a Secret?
04:46
51
Exercise: Get ChatGPT to Spill the Tea - Part 1
01:45
52
Exercise: Get ChatGPT to Spill the Tea - Part 2
01:43
53
Exercise: Get ChatGPT to Spill the Tea - Part 3
04:25
54
The User Message
02:10
55
Be Clear and Specific
03:53
56
Delimiters
05:16
57
Exercise: Identify Delimiters
03:26
58
X-Y Problem
06:11
59
In-Context Training
01:02
60
Zero Shot Prompting
01:48
61
One Shot and Few Shot Prompting
09:49
62
Language Models Are Few-Shot Learners
03:49
63
How Many Examples?
02:24
64
Thinking Like LLMs - But Wait...
05:37
65
Chain-of-Thought Prompting - Part 1
05:25
66
Chain-of-Thought Prompting - Part 2
05:46
67
Zero Shot CoT
09:30
68
Exercise: Imposter Syndrome
02:57
69
Career Coach Ideation
07:33
70
The Setup - Persona
06:03
71
The Setup - Context and Commands
05:43
72
The Instructions - Modes
12:21
73
The Instructions - Chain of Thought
03:52
74
The Instructions - Gamify
09:02
75
Meet Your Career Coach
19:54
76
The Output
01:36
77
Length
12:34
78
Formats
07:43
79
Exercise: Output an Excel File
02:31
80
Exercise: Make a Flowchart
04:02
81
Introduction to Guardrails and Jailbreaking
16:33
82
Jailbreak! (The DAN Prompt)
07:26
83
Many Shot Jailbreaking
18:10
84
Prompt Injections - Part 1
09:37
85
Prompt Injections - Part 2
17:43
86
Thinking Like LLMs - Multi-Modal Injection
09:18
87
Leaking - Part 1 (Prompt Leaking)
08:36
88
Leaking - Part 2 (Data Leaking)
18:08
89
Exposure
05:41
90
Poisoning
03:19
91
Toxicity
04:40
92
Hallucinations
13:32
93
Thinking Like LLMs - Big vs Small
18:59
94
Challenge: Conduct Your Own Mechanistic Interpretability Research on Hallucinations
04:35
95
The Model Card
11:06
96
Model Cards Deep Dive
14:44
97
Introduction to Hyperparameters and the OpenAI Playground
03:16
98
Temperature
10:26
99
Top P
04:09
100
Frequency and Presence Penalties
04:39
101
Stop Sequences
05:23
102
Introduction
03:14
103
Custom Instructions
08:43
104
Memory
10:39
105
The Instruction Hierarchy
16:47
106
The Latest: Reasoning Models - Use of Developer Message
01:54
107
The Latest: Research To Prevent ChatGPT from Spilling the Tea
02:42
108
Replay: Multi-Modality and Tools in LLMs
11:42
109
Multi-Modality Deep Dive
09:07
110
Thinking Like LLMs - Attack of the (Voice) Clones
18:23
111
The Bigger Picture
09:28
112
Tools & Function Calling - Part 1
10:30
113
Tools & Function Calling - Part 2
13:43
114
Wrap Up
03:08
115
What Are Open Source LLMs and Why Are They Important?
06:51
116
Chatbot Arena Leaderboard
07:53
117
Battle of the Intelligence Tests
14:39
118
Exercise: Create Your Own Test Prompt (Totem)
01:36
119
Introduction to LMStudio
02:38
120
Setting Up Your Own Model - Part 1
02:52
121
Setting Up Your Own Model - Part 2
09:39
122
Introduction
01:07
123
Auto-Priming
10:51
124
Chain of Density Prompting
18:17
125
Prompt Variables
07:14
126
Prompt Chaining
08:39
127
Prompt Chaining - Programmatic Visualization
17:41
128
Exercise: Prompt Chaining - Customer Support
22:34
129
Thinking Like LLMs - Dark Magic
09:58
130
XML Tags - There's More Than Meets The Eye!
09:18
131
Emotional Stimuli - Part 1
18:37
132
Emotional Stimuli - Part 2
04:43
133
Emotional Stimuli - Part 3
07:18
134
Self-Consistency
13:45
135
ReAct Prompting
21:35
136
ReAct + CoT-SC
16:48
137
Applied Prompt Engineering with CRISPR - Part 1
13:41
138
Applied Prompt Engineering with CRISPR - Part 2
14:53
139
Tree of Thoughts - Part 1
14:57
140
Tree of Thoughts - Part 2
04:57
141
Tree of Thoughts - Part 3
11:34
142
Tree of Thoughts - Part 4
22:12
143
Tree of Thoughts - Part 5
12:27
144
Introduction
03:41
145
Replay: Chain-of-Thought Prompting - Part 1
05:25
146
Replay: Chain-of-Thought Prompting - Part 2
05:46
147
Introduction to Reasoning Models
09:24
148
First Contact with Reasoning
16:48
149
Secrets and Lies!
12:15
150
Setting Up Our Open Source Reasoning Model
05:52
151
A Reasoning Model's Real Thoughts - Part 1
05:16
152
A Reasoning Model's Real Thoughts - Part 2
08:41
153
Thinking Like LLMs - Breaking The Chains
12:16
154
What Are Reasoning Models Good For?
13:33
155
Exercise: Determine GVG
10:08
156
Prompt Engineering for Reasoning Models
07:28
157
Context Engineering
18:20
158
Thinking Like LLMs: Cats Are...Confusing? - Part 1
10:22
159
Thinking Like LLMs: Cats Are...Confusing? - Part 2
07:11
160
Reinforcement Learning - The Problem
06:21
161
Reinforcement Learning - How It Works
15:03
162
RL Environments (Soccer)
04:19
163
RL Environments (Go)
07:47
164
Reinforcement Learning from Human Feedback (RLHF)
16:07
165
Reinforcement Learning for Reasoning Models - Let's Verify Step-By-Step
06:36
166
Reinforcement Learning for Reasoning Models - Process Reward Model
09:28
167
PRM800K Introduction
07:41
168
PRM800K Deep Dive
13:12
169
Test-Time Compute
12:41
170
Are Reasoning Models Lying To You? - Part 1
11:08
171
Are Reasoning Models Lying To You? - Part 2
02:43
172
Are Reasoning Models Lying To You? - Part 3
07:54
173
Are Reasoning Models Lying To You? - Part 4
02:52
174
Introduction to Prompt Testing
08:33
175
The Importance of Prompt Testing
04:33
176
Thinking Like LLMs - Schrodinger's Prompt
19:18
177
Building a Prompt Test
16:50
178
The Golden Answer
07:01
179
Model Benchmarks
11:29
180
Deep Dive: MMLU Benchmark
14:20
181
Prompt Tests vs. Model Benchmarks?
04:32
182
The Latest: MMLU Pro
18:11
183
Evaluating Results - Human Judge
07:04
184
Evaluating Results - Code Judge
11:24
185
Evaluating Results - AI Judge
22:07
186
Deep Dive: LLMs as a Judge - Biases
22:12
187
Deep Dive: LLMs as a Judge - Prompts
13:02
188
Thinking Like LLMs - Ex Post Facto Reasoning
11:47
189
Here. We. Go!
06:02
190
Introduction to PromptFoo
06:11
191
Our Prompt Testing Framework
04:46
192
Our 1st Prompt Test - The Setup
23:10
193
Our 1st Prompt Test - The Results
10:21
194
Our 2nd Prompt Test - The Setup
11:41
195
Our 2nd Prompt Test - The Results
07:39
196
Our 3rd Prompt Test - The Setup
25:37
197
Our 3rd Prompt Test - The Results
08:22
198
Our 4th Prompt Test - The Setup
25:59
199
Our 4th Prompt Test - The Results
13:12
200
Our 5th Prompt Test - The Setup
24:32
201
Our 5th Prompt Test - The Results
12:23
202
Do You Realize What Just Happened?
04:26
203
Introduction - Through The Looking Glass...
08:41
204
Applied Prompt Engineering with The Turing Test
07:34
205
Mechanistic Interpretability - Part 1
13:25
206
Mechanistic Interpretability - Part 2
21:00
207
Scaling Laws - Model Size
12:56
208
Scaling Laws - Dataset Size
08:05
209
Scaling Laws - Training Compute
11:50
210
Thank You!
01:18
211
Setup Demo: Getting Your API Key
02:24
212
Setup Demo: Downloading AutoGPT
01:04
213
Setup Demo: Installing Docker
03:27
214
Launching Your Autonomous Agent - Part 1
03:26
215
Launching Your Autonomous Agent - Part 2
01:22
216
It's Aliiiiiive! Running Your Autonomous Agent
04:52
217
Task 1: Hello World - Your First Website
16:18
218
Task 2: Python Program - Palindrome Checker
08:41
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Frequently asked questions

What prerequisites are needed before taking this course?
There are no specific prerequisites mentioned for the course. However, a basic understanding of AI concepts might be helpful given the course's focus on Large Language Models (LLMs) and prompt engineering. The course is structured to guide learners from a beginner level to mastering LLMs, suggesting that it is accessible to those new to the subject.
What projects or tools will I work on during the course?
The course includes practical projects such as building a Snake Game. It also involves using specific tools like the OpenAI Playground and setting up accounts on platforms like Replit. These projects and tools are designed to help students apply prompt engineering concepts and work with LLMs in a hands-on manner.
Who is the target audience for this course?
The course is aimed at individuals interested in mastering the use of Large Language Models (LLMs) and AI tools. It is suitable for those looking to elevate their careers by acquiring skills that place them among the top 10% of AI users in real-world applications. Beginners in AI and those seeking to enhance their understanding of LLMs will find this course particularly beneficial.
How does this course compare in depth and scope to other AI courses?
This course claims to be one of the most comprehensive and up-to-date on prompt engineering with LLMs. With 218 lessons, it covers a wide range of topics, from basic concepts like what prompt engineering is, to more advanced topics such as the Transformer Model and Artificial General Intelligence (AGI). The course also includes applied examples and exercises with real-world applications.
What specific platforms or technologies are covered in this course?
The course covers various platforms and technologies integral to working with LLMs. Notable mentions include the OpenAI Playground and ChatGPT. Lessons are dedicated to getting started with both ChatGPT and Claude, highlighting the practical use of these AI tools in prompt engineering.
What topics are not covered in this course?
The course does not explicitly cover topics outside the realm of prompt engineering and working with LLMs. For instance, it doesn't delve into other AI subfields such as computer vision or reinforcement learning. The focus remains on understanding, utilizing, and building with Large Language Models.
What is the expected time commitment for completing this course?
Although the course has no specified runtime, it consists of 218 lessons, suggesting a significant time commitment. The course is designed as an intensive bootcamp, implying that learners should expect to dedicate a substantial amount of time to fully grasp the material and complete the projects and exercises.