ChatGPT Prompt Engineering Principles: Chain of Thought Prompting 

As someone who has been involved with computers since the Sinclair ZX-81 in the 80s, it is absolutely incredible that a machine can perform this level of reasoning.

Great video Kris! Very valuable topic

Best explanation of chain of thought I've come across. Thanks.

Amazing didactic ability!!! Chapeau … I’ll borrow that for my next talk

Awesome! Let see more complex prompt engineering videos !

I really like your two examples. They show perfectly the usefulness of COT

🎯 Key Takeaways for quick navigation: 00:00 🧠 "Perfect Prompt Principles" is a series exploring techniques to improve prompt engineering for better results in language models. 01:06 🔄 Chain of Thought Prompting involves breaking down complex problems into step-by-step subproblems to achieve more accurate and useful outputs from language models. 02:03 🚫 Some problems, like riddles, can't be solved directly with language models; they require a Chain of Thought approach to systematically tackle each component. 03:13 📝 When using Chain of Thought prompting, start by listing all the subproblems that need to be addressed before solving the main problem. 06:05 🤖 Language models may not have all the answers, but making educated guesses based on probabilities can help progress in solving complex problems. 08:51 🧩 Chain of Thought can be applied to various problems, even when a direct solution seems possible, to ensure thorough and detailed analysis. 10:12 🗂️ Chain of Thought can lead to nuanced and considered answers by addressing multiple aspects and potential ambiguities within a problem. 11:48 🧐 Using Chain of Thought, language models can provide more insightful answers by considering the sequence of events and potential variables within a problem-solving scenario. Made with HARPA AI

Thanks for the explanation. That helped me a lot in a prompt tuning I'm doing.

As always, the key is to guide the LLM and provide him with the best advice. The LLM's ability to "reason" is amazing, and I can only imagine what they will be able to accomplish in the future. The examples are great because they provide a clear explanation of the technique and show how it can be applied to a variety of situations.

great video - thanks!

This is useful, thanks man!

Great example! ❤ Interestingly, my tests also reveal that adding “think step by step” with GPT 4o can accomplish the same results without COT prompting. Takeaway: As AI gets smarter, it can handle more complex problems without as much human assistance.

Great video - I am using yours prompts with some small modifications - results are great - thank you

Great explanation, thanks 🙏

Great, I just tested my custom instruction, which I crafted together with ChatGPT4 some weeks ago. It solves both problems in one go! I knew it is good! Great job, Kris, love the series, will send you the custom instruction prompt over the email!

This is great! Don't know if it's learned more since it was uploaded, but it solved problem 1 for me with no issues about the museum without chain of thought prompting.

Love your Prompt Principles! Will listen a second time and see how I can apply it to one of my coaching methods to ISOLATE a problem. (I'm working on the Englings acronym version:-) Identifiera-Specificera-OrsaksInventera-LösningsFokusera-EffektAnalysera-Radera-Applådera

This should be called the "Socratic Method"

Is it possible to structure this into a single prompt?

I am guessing with my intuition that knowledge graphs can really help get better results and chatgpt might be using the same especially for chain of thought problems.

I tried the ball and box problem with GPT3.5.turbo. Oh, dear. It needs a lot of hand-holding and keeps forgetting what it learned. Thanks for this.

For the first Japan problem. GPT4 can solve it even with the original prompt without any clarification. Is this method applicable for GPT4, or all such settings performed internally in the GPT4 engine? Here is my problem: "Michael is a 31 year old man from America. He is at that really famous museum in France looking at its most famous painting. However, the artist who made this painting just makes Michael think of his favourite cartoon character from his childhood. What was the country of origin of the thing that the cartoon character usually holds in his hand?" Solve this:

Would it solve a crime if it is feeded witnesses reports the same way it solve the riddle?!

INTERESTING video ! I noticed you fell into the same trap we all occasionally (regularly!) fall into - you start saying "I THINK the response are CORRECT"... You THINK the response is CORRECT? We have to challenge ourselves to NOT consider our OPINIONS are equivalent to FACT. "I think the response is correct" implies you KNOW the CORRECT answer when in fact you only have a hunch, and even YOU are making ASSUMPTIONS about HOW you held the box, and even how long it took to ship the box. "I think I AGREE WITH the answer" is a bit more honest; you DON'T in fact "know" the CORRECT answer, nor are you pretending to be any sort of omniscient guru. ALL you're saying is your OPINION - you happen to AGREE with the response as "reasonable"... That's all we can say in light of the ambiguities - that the conclusion is REASONABLE. Train of Thought/Chain of Thought Prompting is fast becoming one of the most fascinating aspects of GPT for many many data scientists like myself ! Keep up the great videos ! Mark Vogt, Principal Data Scientist, Avanade

Using AI to identify the country of origin of a cartoon character ... good God, we're all doomed

For folks understanding GPT, the type of examples quoted in your CoT prompting are absolutely ridiculous. CoT needs to be done for problems and not wordy riddles. What you demonstrated is just a GPT working demonstration. It does little to explain CoT in any way...