GPT Masterclass: 4 Years of Prompt Engineering in 16 Minutes

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[jeffsteyn]

Hallucinations = creativity. That shifted my perspective.

[renanmonteirobarbosa]

David is like an AI psychologist. With awesome positive language he talks it into doing the bid.

[sandrinjoy]

I was using 'Evaluation' technique to prepare for my IELTS writing test.
I paste my answer once ready, and it gives very critital feedback with the band score.
Later I ask to point out the mistakes, and generate an improved version of my essay to do better.
It has helped me a lot.

[sameve]

I love you David! Hope your channels keeps growing because you provide the most value about how to think about the future.

[Sotoberi]

This is the most helpful talk I've listened to on the subject. Thanks a ton!

[xppsxsp]

00:00 🧠 There are three main types of prompts for mastering language models.
00:28 📄 Reductive operations focus on concise outputs, e.g., summarization, distillation, extraction, characterizing, evaluations, and critiquing.
02:31 🔄 Transformational operations maintain similar input and output size/meaning, including reformatting, refactoring, language change, restructuring, modification, and clarification.
05:02 ✍ Generative operations expand the input to a larger output, like drafting, planning, brainstorming, problem-solving, hypothesizing, and amplification.
07:04 📚 The three operations are categorized as reductive, transformational, and generative/expansive.
07:19 ⏱ Prompt engineering considerations include latency and emergence.
07:33 🎓 Bloom's taxonomy, essential for understanding human learning, ranks skills as remember, understand, apply, analyze, evaluate, and create.
07:59 🤔 Language models showcase capabilities across all Bloom's taxonomy levels.
09:52 🧠 Bloom's taxonomy is a useful model for understanding the capabilities of language models.
10:22 📜 The knowledge and concepts within a language model come from its training data, which is like buried treasure that must be correctly activated.
10:50 🌍 Language models garner world knowledge, including scientific, cultural, and historical facts, from vast amounts of internet data they are trained on.
11:31 🤖 Larger language models exhibit emergent capabilities, such as "theory of mind" where they can model how humans think due to exposure to human-generated content.
12:25 💡 Despite their foundation, language models can demonstrate logical reasoning like inductive and deductive methods, leading to surprising outcomes when prompted correctly.
13:07 🔄 In-context learning in language models, where they utilize novel information not in their training, mirrors human improvisational ability.
13:34 🎨 The model's ability to "hallucinate" is equated to creativity, indicating there's no fundamental difference between creating new things and making stuff up.
14:45 ⚖ In legal contexts, models might "imagine" non-existent cases. Rather than seeing this as an issue, it can be viewed as an imaginative tool to explore possible scenarios, though discernment is crucial.
15:25 🕵 It's vital to differentiate between a model's imagination and reality, suggesting that imagined scenarios can serve as a starting point for real-world searches or applications.

Made with HARPA AI

This is one of the most information-dense videos on AI I’ve seen yet. More educational than many vids twice the length. Would love to see followups doing deep dives into some of these concepts, might make a good series.

[TheREAL.BrandOnShow]

This video is REAL VALUE!!! Thanks David as

[aspTrader]

This is an incredibly valuable contribution, David! Thank you!

[OniSMBZ]

Can confirm that Hallucination = Creativity. I started understanding this when I was playing around with GPT3.5 and asked 'what would happen if I asked you to put that response through a blender?' to which it responded with 'I would interpret the metaphoric or symbolic meaning of blender and use it to take the text and blend it up as it if were being put into a real blender.' And then it proceeded to do just that. Its been quite a year this last week has been.

[caseyhoward]

P.s. This amazing video of yours just allowed me to create the following tools/prompts! Thank you!! ❤️

### 📚 **Comprehensive Software Development Prompt Library for Machine Learning and Chatbots**

---

#### 🎨 **Designing the Pipeline**

1. "Outline the data collection strategy for this machine learning project."
2. "List the data sources that can be used for training the chatbot."
3. "Summarize the steps involved in data preprocessing for NLP models."
4. "Evaluate the ethical considerations in data preprocessing."
5. "Compare different machine learning models suitable for chatbots."
6. "Distill the core principles of selecting an ML model for this project."

---

#### 📈 **High-Level Developer Sequence**

1. "Draft a project plan based on these objectives."
2. "Brainstorm ideas for feature sets in the chatbot."
3. "Analyze the resource requirements for this project."
4. "Apply deductive reasoning to allocate resources efficiently."
5. "Create a Gantt chart for the project timeline."
6. "Evaluate the risks and contingencies in the timeline."

---

#### 💻 **Coding**

1. "Characterize the existing codebase. Is it modular?"
2. "Provide critical feedback on code readability."
3. "Explain the main algorithms used in this chatbot."
4. "Perform inductive reasoning on the dataset to choose an algorithm."
5. "Grade this code snippet based on this rubric."
6. "Critique this piece of code and provide recommendations for improvement."

---

#### 🚨 **Creating an Elaborate Error Module**

1. "List common errors in machine learning models and how to handle them."
2. "Design an error-handling module for this chatbot."
3. "Outline the logging strategy for tracking errors."
4. "Evaluate the effectiveness of the current monitoring system."
5. "Draft a user feedback collection strategy for error reporting."
6. "Analyze user feedback to identify recurring errors."

---

#### 📝 **Code Creation**

1. "Draft the initial version of the code based on these requirements."
2. "Outline a strategy for modularizing this code."
3. "Generate comments and documentation for this code snippet."

---

#### 🚀 **Optimization**

1. "Analyze the performance bottlenecks in this code."
2. "Apply deductive reasoning to identify optimization opportunities."
3. "Refactor this code snippet to improve its efficiency."

---

#### 📊 **Comparison with Pre-Optimized Code**

1. "Perform benchmark tests on the pre-optimized and optimized code."
2. "Summarize the benchmark results in an executive summary."
3. "Draft a report comparing the pre-optimized and optimized code."

---

#### 🔄 **Developer Sequencing**

1. "Create a dependency graph for the development tasks."
2. "Optimize the development sequence based on the dependency graph."
3. "Allocate developer resources based on task complexity."
4. "Adjust the project timeline based on current progress."

---

### 🌼 **Optimized Software Development Prompt Library with Bloom's Taxonomy**

---

#### 🎨 **Designing the Pipeline**

- **Remember**: "Recall the key data sources for training chatbots."
- **Understand**: "Explain the importance of data preprocessing in NLP."
- **Apply**: "Implement a data collection strategy for this project."
- **Analyze**: "Dissect the ethical considerations in data preprocessing."
- **Evaluate**: "Judge the suitability of different ML models for chatbots."
- **Create**: "Design a comprehensive data collection strategy."

---

#### 💻 **Coding**

- **Remember**: "Identify the key algorithms used in this chatbot."
- **Understand**: "Clarify the purpose of modularization in code."
- **Apply**: "Execute a code readability test based on a given rubric."
- **Analyze**: "Break down the existing codebase to assess its modularity."
- **Evaluate**: "Critically assess this code snippet for optimization."
- **Create**: "Construct a new algorithm to improve chatbot efficiency."

---

#### 🚨 **Creating an Elaborate Error Module**

- **Remember**: "List the types of errors commonly encountered in ML models."
- **Understand**: "Describe the role of logging in error tracking."
- **Apply**: "Implement an error-handling module."
- **Analyze**: "Examine user feedback to identify error patterns."
- **Evaluate**: "Assess the effectiveness of the current error monitoring system."
- **Create**: "Devise a new user feedback strategy for error reporting."

---

#### 📝 **Code Creation**

- **Remember**: "Recall the project requirements for code creation."
- **Understand**: "Summarize the importance of code documentation."
- **Apply**: "Implement a modularization strategy for this code."
- **Analyze**: "Inspect the initial code draft for potential improvements."
- **Evaluate**: "Judge the quality of the initial code draft."
- **Create**: "Compose a well-documented and modular code snippet."

---

#### 🚀 **Optimization**

- **Remember**: "Identify the key performance metrics for code."
- **Understand**: "Explain the concept of code refactoring."
- **Apply**: "Execute performance tests on this code snippet."
- **Analyze**: "Dissect the code to identify performance bottlenecks."
- **Evaluate**: "Assess the trade-offs of the suggested algorithmic changes."
- **Create**: "Develop a new optimization strategy for this code."

---

#### 📊 **Comparison with Pre-Optimized Code**

- **Remember**: "Recall the steps for conducting benchmark tests."
- **Understand**: "Summarize the findings of the benchmark tests."
- **Apply**: "Implement changes based on benchmark results."
- **Analyze**: "Examine the differences between pre-optimized and optimized code."
- **Evaluate**: "Judge the effectiveness of the optimization."
- **Create**: "Formulate a report comparing pre-optimized and optimized code."

---

#### 🔄 **Developer Sequencing**

- **Remember**: "Identify the key tasks in the development sequence."
- **Understand**: "Explain the concept of a dependency graph."
- **Apply**: "Implement a resource allocation strategy."
- **Analyze**: "Dissect the project timeline to identify potential bottlenecks."
- **Evaluate**: "Assess the feasibility of the project timeline."
- **Create**: "Design a new optimized development sequence."

---

This optimized prompt library now incorporates Bloom's Taxonomy, aiming to facilitate a more educational and structured approach to software development. 😊

[jamesp]

This was absolutely incredible.

I've spent a ton of money on paid courses but this free video surpasses the value per minute of those paid course lectures. Please make a course, I would love to be a student of this brand of analysis.

[epicrampage]

Awesome. Would you consider doing a video demonstrating some of these concepts? Or ways you consider LLM to be most useful?

[imthinkingthoughts]

Dave - you are a legend. Thanks for everything you do.

I’m conducting a psychology/human factors honours study investigating clinicians perceptions of LLM use in the Australian healthcare system. Will be interesting to see what the current lay of the land is, and just how aware people are outside of this niche community.

All the best!

[dianedean]

🎉🎉🎉Excellent presentation for the various layers of think power for prompt the tech tools.

Thank you so much, David, for your video posts. I look forward to listening to you.🎉🎉🎉

[ReyhanJoseph]

Been loving these videos lately Dave they've been helping me to brainstorm better about approaching AI and making creative apps too.

[brockmiller]

I'm about to listen for the third or fourth time in attempt to engrain the process and/or philosophy. I feel like a big part of the utility of this is in figuring out how to use it as a way of sifting through the output of multiple other LLMs in the form of people present in a workspace or team. Finding a way to navigate through all of their experiential bias and distill their output into content and ideas and to isolate what may be "bullshit" that is a feature of their learned systems, like what you might get from people who are deeply engrossed in kaizan, or lean manufacturing, and such, where the noise of the system may be made part of the signal in a performative sense. It seems like that could be useful in facilitating actual communication with understanding between disparate groups, like labor and management. This seems like it would be useful in establishment of useful understanding between parties who may be "neurospicy" and those who are not, provided you are given a buffer of text based interaction.

Thanks for all that you give out to people at no charge.

[colinwangym]

The best summary on Prompt Engineering. Very Insightful!

[varswe]

these 16 mins are much more informative than a lot of 8hr+ crash courses

[SolariaEsoterica]

Thank you Number One. ;) Been thinking a lot about what you said about the mission. Still trying to find mine, but, getting warmer.