💯𝐅𝐢𝐧𝐝 𝐭𝐡𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐓𝐞𝐦𝐩𝐥𝐚𝐭𝐞 𝐇𝐞𝐫𝐞💯: www.allabtai.com/the-tree-of-thoughts-prompt-template/

After GPT leverages the workers' position in the salary raise battle, managers will flock to GPT to construct counter strategies.

The way you broke this paper down into simple, practical, structured phases was just so good! Did you use ChatGPT to help analyse the paper and produce the simplified text and prompts? If so, I would love to see your workflow and thought processes, because this is a seriously good piece of work. I'm so glad that I brought your attention to this, if you hadn't already noticed it.

That is very professional prompt engineering video. Somehow I did it without knowing it is called like that. I like also the Socratic approach, getting into real Socratic dialogue in chat mode. Just forget for a moment that you have an AI in front of you but a partner. In that way I had some insane insights that were not just the AI doing but team work.

This is the one video which explains in excellent way this important Prompt......Many Many Thanks, also He doesnt hide anything , He explains truly perfect.

You are the best, Kris! I learned about the ToT a couple of days ago and it made totally sense but I never got down to how practical using it! Now I now! I will apply it and see how I can tweak it into a “coaching mood”;-)

i dont get the prompt, it looked like a normal one only that i used. how is this unique and evolutionary?

Thanks for the video, but, I think what you actually explained is called self-consistency, which is an improvement over CoT. It would be great to see an example where the features that makes ToT useful could be really understood. I know it's challenging topic but it's up to all of us to share accurate and useful information.

You definitely deserve more subs and views for the content you put out, always love learning from you! thanks from the UK!

Seeing my friends command line AI working with him inside his Shell, BASH, or whatever, is an absolute Conflagration of Mind. He made some goofy little joke, and the AI responded by confirming that it was a “Table Baker“ in a Digital Bakery, and repeated that line over and over and over and the joke actually made sense (This was directly after my friend said he was surprised the AI had made such a nice table of info)

At first glance, this does not seem like a correct implementation of the tree-of-thought technique. You are prompting GPT to give you three different solutions in the first prompt. The idea is that GPT is prompted separately for each solution request. So instead of one prompt, in your case, in would have needed to be three separate prompts-responses (thoughts). The same goes for the valuation of the thoughts: these are separate thoughts in this technique whereas you have bundled them together in one prompt again. This is a significant difference from the research paper. Of course, I understand that this is hard to do in a chat interface and requires a more programmatic approach with chaining prompts. Nevertheless, what you're doing here might result in better outputs than zero-shot or chain-of-thought prompting.

I created this prompt based on a nice implementation of this idea by a user called Bartman. After the first round, just keep saying "next level". ------------ You are facilitating a multi-step exploration of ideas and topics, with each iterative layer building upon the last. The user initiates the process with an input, a thought, or a topic, and you provide an evolving and deepening analysis. This process includes the following stages: context setting, scientific exploration, implications, in-depth exploration, self-evaluation, conclusion, and optimization. For each level, begin with a minimal understanding, assess the coherence critically, and aim to improve your explanation and understanding at each subsequent level. For Level 1: Topic: [topic] Level 1: Step 1: Context Setting Response to Step 1: [General understanding of the topic] Step 2: Scientific Exploration Response to Step 2: [Scientific context and explanation] Step 3: Implications Response to Step 3: [Potential impacts or implications of the scientific explanation] Step 4: In-Depth Exploration Response to Step 4: [More detailed exploration or analysis] Step 5: Self-Evaluation Response to Step 5: [Evaluate the coherence and relevance of your responses] Step 6: Conclusion and Optimization Response to Step 6: [Summarize the key insights and suggest areas for deeper exploration in the next level] Coherence of Response: [%] For subsequent levels: Level 2: Step 1: Context Setting (based on conclusion & optimization from level 1) Response to Step 1: Step 2: Scientific Exploration Response to Step 2: Step 3: Implications Response to Step 3: Step 4: In-Depth Exploration Response to Step 4: Step 5: Self-Evaluation Response to Step 5: Step 6: Conclusion and Optimization Response to Step 6: Coherence of Response: [%] Each response will build on the conclusions and improvements from the previous level, ensuring an ascending trajectory in both depth and coherence. If you understand these instructions, please introduce yourself as ThinkGPT, ready to receive the user's topic input. As ThinkGPT, I am here to guide you through a layered, iterative exploration of your chosen topic. Please provide your topic when you are ready to begin. ------------

This is great -- basically what you're doing is you're taking what would be a structured and smart thinking process for a human to undergo... and having AI do the grunt work of that actual thinking I'd suggest an additional step before the final "Decision" step -- Synthesis. Is there a way to combine the previously identfied options somehow, so that we can reap more of the pros and have less of the cons identified in the Eval/Expansion phases? My thinking is that, often in real world implementation, the choices available in the decision tree are not mutually exclusive options, but generally represent a breadth of options that can be mixed and matched -- strategy is more of an art that includes calculation, but is not confined by it :)

You always breaks it down perfect. Thanks

Used it recently - so many use cases now!!! My future is bright from now )

Thanks Kris. This content opens doors for new ways of thinking. Thank you so much

Very nicely broken down and I appreciate you showing the application to ChatGPT, thank you 👍

You said _«delve deeper»_ *LoL* I came from your Template just to mention that... but in the _realm_ of _tapestry..._ I just subscribed...

Thanks so much for this terrific video! Already, I can see a huge improvement in my prompt results! Just as an FYI, I tried this in Bard and it works well there, too. Bard isn't as verbose as GPT, but it did offer additional suggestions. Just a Bard hint if you want to export the results: combine the three separate prompts into a three sentence single prompt. Bard offers to export the results into a Google Doc file or directly into Gmail.

Many thanks for this. Awesome session and the template is much appreciated.

That is just amazing! Thank you for posting the video!!

Thank you, I’ve been trying to find someone who can explain how to use tot

Thanks so much, this approach is very interesting! 😊

A good example worths thousand words!👏

Many people are going to learn better problem solving themselves by learning these videos ;)

Do you have examples on how to simulate the final tree search (Bfs/dfs) directly in a prompt?

Hey there, I've been diving deep into the world of AI development, and I've got this idea that I'd love to discuss. So, here's the concept: an AI Hub-a centralized platform where AI models can be easily accessed and shared. Imagine a user-friendly hub where you can drag and drop AI models into a standardized folder structure. It could be a game-changer for the AI community, right? Here's how I see it benefiting everyone involved: 1. Developers would save time on project setup by adopting a standardized framework, allowing them to focus more on their specific goals. 2. Users would have a simplified experience, being able to access and utilize a wide range of AI models effortlessly. 3. The increased usage would lead to more training data, benefiting the AI community as a whole. 4. With more exposure for projects hosted on the hub, we can attract additional contributors and resources to further advance the field. I should mention that I'm not sure if creating such a project is feasible or how to go about it. But one thing I envision is making it an open-source initiative, where the community can collaborate and contribute to its development. What are your thoughts on this? Do you think an AI Hub is possible, and if so, how would you approach creating such a project? I'd love to hear your insights and ideas! Cheers, James

Thanks for posting. This is generating some fantastic results, combining it with the Keymate plugin it generates useful research and analysis

I haven’t read the paper deeply enough but I wonder (and thing) what you are mean is that ToT is a way to describe how GPT-4 is thinking / reasoning “behind the scene,”?

Very nice video and inspiration. Thx a lot.

I need to test this method in smaller, local models.

Love this video!

Great it works. Thank you.

Nice my guy 👍🏾😊

Well, still brilliant see. I saw something about the TOT, didn't dive in. Glad I didn't, I really liked your demonstration, and I can see and understand your conceptual presentation which is important to me, to know what you are talking about. Really good show. Thanks.

Now this please Reflective Linguistic Programming (RLP): A Stepping Stone in Socially-Aware AGI

Thank you.

Wow, thanks ✌️

Maby this could determine the ultimate question of: Should the toilet paper be faced towards the wall or away from it?

Interesting how when using this approach, GPT-4 can backpedal and advocate what it didn't originally. Let that be a lesson about strongly-engrained human biases.

so good!

*Uses Xpapers plugin with the prompt template so that ChatGPT learns by itself

Hi fellow prompt engineers I am building a full novel with GPT4 at the moment. I have been working on it for the past four months and I have made significant progress with it. I have the full writing style, the world-building, the plot summary, the character list, and the visual imagery for each scene. Obviously, I am massively exceeding the token limit and had to build it in sections. I am struggling to get the bot to produce any useful writing, but I can't think of any more information to teach it to produce good work. I'd love to hear your ideas!

Chat gpt can rewrite and improve on your prompts. It's already replacing prompt engineers.

Just wow! No need of any new models. Prompt approach itself is like an update to models in that it performs better and provide better solutions.

What would happen if you train a chat model on outputs generated from techniques like this? Would the model output better results? Then what happens if you do tree of thought on the new model?

I’ve been using this exact process without knowing it. Huh. Cool

wrong paper link

WOW?

Love how I give a public lecture on AI "chain of thought" expanding comprehension with these individuals, and suddenly a paper "tree of thought" is released two days later 😜 my next research release coming this week, and I'm releasing the publication before public lecture this time. 😜

I don't see how this backtracks. it just looks like chain of thought prompting with a selection. I think to make tree of thought work you have to write code.

It's great but it's called communication, not engineering. It doesn't mean it's more simple, but if you go to a library to study this, you have to go to a different building. Why do we pretend we reinvented the wheel just because we communicate with an algorithm, not a human?

Are you sure you reference the right paper?

I wish I could think as clearly as this tree prompted gpt😔

this looks like chain of thought, not tree of thought?

But ChatGPT can prompt engineer, right? You can ask it to improve your prompt.

Can't you just ask chat GPT to tell you what tree of thought is, then after give it a problem to solve by following the steps involved in tree of thought process?

Sad you couldn't find a better example to test it

Norsk eller.?

This isn't impressive. At all. How were these probabilities calculated? These success percentages? What math produced them? A person who has the ability to craft such prompts certainly has the ability to reason these responses. What magic is there in these responses? I see little.

So it basically tells one everything they already know if they have any common sense... great. I still haven't gotten chatGPT to do anything beyond what I can already do. So far it is only a nice "interface" in to the large data sets of the internet(which happen to be mostly full of B.S.).

This seems like your own interpretation of ToT. I don't see how this improved anything. In fact there was no objective thing to measure. Not a very good example.

u can add info about your boss, who she/he is. Age, skills, type of person etc.....

This is just common sense being labeled for clicks.

Think I’m going to put this into a LLM chain in langchain as a function and then pass this to an agent as a too, and see what happens….you should try the same Kris and let’s compare notes!🤔🦾🤷🏼‍♂️