English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 220 lectures (22h 38m) | 10.48 GB
Learn practical coding skills for working professionally with AI, including GPT-4, Stable Diffusion, and GitHub Copilot.
The number of papers published on AI every month is growing exponentially, and it’s becoming increasingly difficult to keep up. The open-source project Stable Diffusion is the fastest growing repository in GitHub in history, and ChatGPT is the fastest growing consumer product in history, hitting 1 million users in less than a week and 100m in a few months.
This course will walk you through:
- Introduction to Prompt Engineering and its importance
- Working with AI tools such as ChatGPT, GPT-4, Midjourney, GitHub Copilot, GPT-4, DALL-E, and Stable Diffusion
- Understanding the capabilities, limitations, and best practices for each AI tool
- Mastering tokens, log probabilities, and AI hallucinations
- Generating and refining lists, summaries, and role prompting
- Utilizing AI for sentiment analysis, contextualization, and step-by-step reasoning
- Techniques for overcoming token limits and meta-prompting
- Advanced AI applications, including inpainting, outpainting, and progressive extraction
- Leveraging AI for real world projects like generating SEO blog articles and stock photos
- Advanced tooling for AI engineering like Langchain and AUTOMATIC
What you’ll learn
- Learn the strengths and weaknesses of ChatGPT, Midjourney, GitHub Copilot, Stable Diffusion & other major models.
- Recognize the “Five Principles of Prompting”, as well as common tips & tricks for professional grade output.
- Apply what you’ve learned to generate new AI products in 15+ real-world projects for both text and image generation use cases.
- Understand the Python coding patterns and tooling you need to run and scale AI reliably in production, and start working as an AI Engineer.
Table of Contents
Introduction
1 Introduction to the course
2 What is Prompt Engineering
3 Accessing resources and prompts
4 Optional videos to only do if you know coding
5 ChatGPT AI Prompt Pack – 690 Effective Prompts
6 Using OpenAI Playground
Five Principles of Prompting
7 Give Direction
8 Specify Format
9 Provide Examples
10 Evaluate Quality
11 Divide Labor
12 Applying The Five Principles + Worksheet & One Pagers
How Does AI Work
13 What are Tokens
14 Log Probabilities
15 AI Hallucinations
16 Chat Models vs Reasoning Models
Standard Text Model Practices
17 List Generation
18 Sentiment Analysis
19 Explain It Like I’m Five
20 Least to Most
21 Writing Clear Instructions – Detailed Instructions
22 Writing Clear Instructions – Specifying the Steps
23 Writing Clear Instructions – Delimiters
24 Writing Clear Instructions – Specifying Length
25 Let’s Think Step by Step
26 Role Prompting
27 Ask for Context
28 Question Rewriting
29 Pre-Warming Chats
30 Progressive Summarization
31 Overcoming the Token Limit in ChatGPT
Advanced Text Model Techniques
32 Meta Prompting
33 Chain of Thought Reasoning
34 Prompt Injection
35 Automatic Prompt Engineer
36 Github Repository for the Course
37 Advanced List Generation – Coding
38 Prompt Optimization – Coding
39 Overcoming Token Limit – ChatGPT – Managing the Message History – Coding
40 Vector Databases – Coding
41 Reason and Act (ReAct) – Coding
42 Recursive Re-prompting and Revision – Coding
43 Information Retrieval with Vector Databases – Coding
44 Structured Outputs for OpenAI – Coding
45 What is Prompt Caching
46 Prompt Caching in Practice – Coding
47 OpenAI Realtime – Example
48 AI Resource Hub
49 Personas of Thought
Deep Dive on LangChain – Coding
50 Prompt-Engineering-Course-What-Is-LangChain
51 What Is LangChain – Coding
52 Installation – Coding
53 Chat Models – Coding
54 Chat Prompt Templates – Coding
55 Streaming – Coding
56 Output Parsers – Coding
57 Summarizing Large Amounts of Text – Coding
58 Document Loaders, Text Splitting & Creating LangChain Documents – Coding
59 Tagging Documents – Coding
60 Tracing with LangSmith – Coding
61 LangChain Hub – LangSmith – Coding
62 LCEL – The Runnable Protocol – Coding
63 LCEL – Chat Models, itemgetter & RAG – Coding
64 LCEL – Chat Message History & Memory – Coding
65 LCEL – Creating Multiple Chains – Coding
66 LCEL – Conditional Logic, Branching & Merging – Coding
67 Using JSON Mode with LangChain – Coding
68 Exercise – Using JSON Mode with LangChain – Coding
69 LCEL – with JSON Mode – Coding
70 LCEL – with OpenAI Functions & JSON mode – Coding
71 Exercise – LCEL – with OpenAI Functions & JSON mode – Coding
72 LangChain Vector Databases + the Indexing API – Coding
73 LCEL Configurable Fields – Coding
74 LangChain Agents & Tools – Coding
Deep Dive On LangGraph – Coding
75 Introduction To LangGraph – Coding
76 Simple LangGraph Flows – Coding
77 Tool Usage and Persistence – Coding
78 Human In The Loop – Coding
79 Manually Updating The State – Coding
80 Customizing State in LangGraph – Coding
81 Time Travel – Coding
82 RAG in LangGraph (Self Corrective RAG)
83 Extra Content To Explore In Your Own Time (Advanced BranchingSubgraphs – Coding
Proven Prompting Techniques
84 Chain of Thought
85 Emotion Prompting
86 Role Prompting
87 In Context Learning
88 Self-Consistency Sampling
Prompt Optimization & Evals
89 What are Evals
90 Prompt Testing in GSheets (without code)
91 LLM & Image Model Performance Advanced Evaluation Strategies – Coding
92 Eval for a RAG system
93 Prompt Optimization with DSPy – Coding
94 Eval metrics with DSPy – Coding
95 Prompt Optimization 5 Principles of Prompting – Coding
96 Prompt Optimization Advanced – Coding
97 Sammo – Introduction
98 Sammo – Metaprompting
99 Sammo – Testing and Optimization
AI Text Model Projects
100 Tell me a funny joke
101 Create an Entire Ebook
102 SEO Blog Articles
103 Thought Leadership Posts
104 Summarizing Text – Coding
105 Summarizing An Entire Book – Coding
106 Review Classification – Coding
107 AI Blog Post Generation – Coding
108 Text To Speech using OpenAI – Coding
109 Using LangChain + Llama3 Locally with LMStudio – Coding
110 Transcribing audio from a Youtube Video – Coding
111 Fine-Tuning on Writing Style – Coding
112 Adcopy Writing – Coding
113 Social Media Posting – Coding
114 Reverse Engineering a Publication – Coding
115 Building a GPT wrapper with Flask and HTMX – Coding
116 Qualitative Analysis- Coding
117 Claim Detection – Coding
118 Summarize a news story
119 Write a PRD
120 OpenAI Realtime – Twilio Example
Standard Image Model Practices
121 Style Modifiers
122 Quality Boosters
123 Negative Prompts
124 Weighted Terms
125 Prompt Rewriting
126 Inpainting
127 Outpainting
128 Realistic Models
129 Consistent Characters
Advanced Image Model Techniques
130 Midjourney Outpainting (Zoom Out Pan)
131 Midjourney Inpainting (Vary Region)
132 Meme Unbundling
133 Meme Mapping
134 Permutations Prompts
135 Prompt Reverse-Engineering
136 Prompt Token Analysis
137 AUTOMATIC1111 – Requires Automatic1111
138 XYZ Prompt Grids – Requires Automatic1111
139 Advanced Inpainting – Requires Automatic1111
140 ControlNet – Requires Automatic1111
141 ControlNet Inpainting – Requires Automatic1111
142 Segment Anything – Requires Automatic1111
143 Textual Inversion – Coding
144 Dreambooth – Coding
145 Migrating to Stable Diffusion XL in AUTOMATIC1111 – Coding
146 Comfy UI
147 Beta – Anthropic Computer Use – Example
AI Image Model Projects
148 AI Custom Illustrations
149 Making a Brand Logo
150 AI Stock Photos
151 Runway – Creating b-roll footage
152 Product Placement – Coding
153 Tagging Ad Creative – Coding
154 AI Profile Picture – Coding
Deep Dive on ChatGPT
155 What is ChatGPT
156 Prompting ChatGPT
157 ChatGPT Capabilities and Limitations
158 ChatGPT Shortcuts
159 ChatGPT Custom Instructions
160 ChatGPT – Memory
161 ChatGPT – Scheduled Tasks
162 ChatGPT – DALL-E 3
163 ChatGPT+ (Code Execution, DALLE, GPTs & Web Browsing Functionality)
164 ChatGPT – GPT-V (Vision)
165 ChatGPT – Interactive Tables
166 ChatGPT – Canvas
167 ChatGPT – Desktop Application
168 GPT Store – Building Custom GPTs – Coding
169 ChatGPT Search
Deep Dive on GPT-4
170 What is GPT-4
171 Prompting GPT-4
172 GPT-4 Capabilities and Limitations
Deep Dive on Midjourney v6
173 What is Midjourney
174 What-is-Midjourney-Google-Slides-13-March-2023
175 Prompting Midjourney
176 Midjourney Capabilities and Limitations
Deep Dive on Anthropic Claude
177 What is Claude
178 Prompting Claude
179 Claude Projects
180 Anthropic Workbench
Deep Dive on Stable Diffusion XL
181 What is Stable Diffusion
182 Prompting Stable Diffusion – Coding
183 Stable Diffusion Capabilities and Limitations
Deep Dive on DALL-E 3
184 What is DALL-E 3
185 Prompting DALL-E 3
186 DALL-E 3 Capabilities and Limitations
Deep Dive on GitHub Copilot – Coding
187 What is GitHub Copilot – Coding
188 Installing Copilot – Coding
189 Prompting GitHub Copilot – Coding
190 GitHub Copilot Capabilities and Limitations – Coding
191 Github Copilot – Editing Features – Coding
192 Github Copilot Chat + Custom Prompts
Multimodal Models
193 Vision Prompting Guide
194 Automating Product Descriptions via GPT-V
195 Automating UX Landing Page Analysis via GPT-V
196 Memetic Analysis with GPT-V
Agent Architectures – Coding
197 Prompt Chaining
198 Routing
199 Parallelization
200 LLM Orchestrators
201 Agents
202 Mixture of Experts – Aggregator
203 Additional Agent Architectures
Deep Dive on other AI Models
204 What is Google Gemini
205 What is Meta LLaMA
206 Runway ML
207 What is Google Vision
208 What is OpenAI Whisper
209 Testing Open-Source Models
210 What is Flux
211 Perplexity Search
AI Tools We’ve Tried
212 Google Gemini – Deep Research
213 Google NotebookLM
214 PromptLayer
215 PromptFoo
216 Instructor
217 Groq Cloud
218 Zapier (no code)
219 Langwatch
Conclusion
220 Free PDF Prompt Engineering Book (CH01)
221 Sources of Inspiration
222 Next steps after the course
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