GitHub Repo: github.com/AllAboutAI-YT/think-before-you-speak

Brilliant as always. Thank you for sharing legend!

Great points- thanks man

Thank you. This is by far the best prompt engineering I tested ! I would like to express to you my gratitude. Can you share more prompt engineering methods ?

That's exactly my experiment ;) I'm trying to simulate the System 1 and SYstem 2 thinking via simulating the conversation flow before the response reaches the user :)

Great video, as always

Yeah claude 3 is awesome! Finally an LLM that does what I want it to do lol

Interesting approach! Thank you for your experiments, I am always happy to see your ideas. I immediately thought of systemic counseling. In short, the counselor would not give advice, but would motivate the client to pursue his wishes by asking open questions or come to the answer himself, since he already has the answer for himself anyway.

you could give more compute to deeper chain of thoughts. Maybe asking several LLMs responses and maybe asking all of them a deeper thought on a subject, and then maybe each of them, assessing each others answers, and then working out the best response. All of this takes a heap of compute though ... but the result maybe a very thorough and concise answer.

I loved the video and definitely learned something there! I just can't seem to connect this kind of double pass prompt technique with the Quiet STaR paper itself, which to me doesn't seem to be a prompting technique but rather a built-in system in the LLM itself

This is exactly what I've been saying. First grade I was asked to read aloud to the class. I had dyslexia but I didn't know it. I decided to read the sentence in my head constructing it before I spoke. When I read it aloud the teacher advanced me to advance reading. It took until third grade to figure out that I had a reading problem but I thought before I spoke with the sentences I read aloud. I guess I faked out the teachers for three grades before they found out I had dyslexia putting me in advanced reading how messed up is that.

Whats GPT 4? Its all about Claude 3 now. :P

It would neat to see any differences in math performance. I want to create a spanish tutor and its helpful to have a ai agent that can count words precisely. Maybe that is a simple prompting issue. One more thought: could this work for image generation?

Kris have you tried the python package "rich"?

cool love the video , i have signed up as a member but i cant see the find the GH repo, what should I do?

This sounds like ideas explored in chain-of-though prompting or multi step prompting. AFAIK there is not way to really have the LLM "think more" about the answer by spending more compute directly on the problem. It will always spens the same amount of compute on the same amount of tokens, thats just how the algorithm works. So if you want it to "think more" about a problem you have to make it generate more tokens. And possibly have it summarize the result in the end. Naturally that will make it more expensive.

Great concept! I think during Nvidia’s most recent GTC keynote, Jenson pointed out that the architecture of the Blackwell GPUs includes a kind of ‘smart router’ on a hardware level, which allocates computing resources based on the task requirements.

And if it’s already answered the question it would have that for reference and the allocation of needed “thought time” would be reduced. Additionally that would be the thought behind why what is a banana being more simple because it’s already answered or has more data behind it. Therefore a new question you asked yesterday would take less time tomorrow after you or others have asked. Collectively it would be understandable for openAI to collect the data as we ask. I think the api doesn’t save questions. You could however for easy reference. Every question with answer could be backed up and have the ai read the questions for easy data retrieval and only grab info from data backup if it’s useful.

We dedicate the same level of compute per token, yes, but one of those questions will yield a longer response for each subsequent token the response-so-far is part of the context. Quite often ChatGPT changes its mind during a response, which is in effect spending more compute for a more complex question. Also, you can already switch compute allocation for different questions by changing ChatGPT models to the crappier-and-faster ones.

great video! is it even possible do give "thinking time" to a LLM today or in the future? what do you think?

great job:) where can i find this code? and will this be a feature in GPT-5?

It would surprise me if they didn't have some method to choose appropriate amount of compute based on token-length. But maybe you are right

yeah this is good:) what would you think is the biggest challenge to create this system? and how do i become a member to get access to the code?

Great

so what is the answer? Options are green, yellow, brown or black

When would AGI start to create its own KZfaq channels

Aww… i was hoping it would use a voice and personality to speak to you. 😊

I really enjoy your channel and I’m interested in experimenting with Quiet-STaR too. I’m sorry to say, but I think that you should spend more time thinking about this. In your first example “What color is a banana?” I believe you are looking for an easy answer of yellow. Great, but this isn’t the correct answer. A banana can be yellow, green, red, brown, black, or a combination of some of those colors. So, I don’t think that the correct answer is as easy as you think that it is. Also a single quick search of Wikipedia for Pythagorean theorem yields multiple examples of proofs. You almost need to know the answer to a question to know if it is an easy or difficult question. Even then, LLMs don’t necessarily process info the same as we do. What is easy for us, could be more difficult for the LLM, and what is difficult for our brain, may be a simple lookup for the LLM. Further, I don’t think that you are being fair with your baseline question. You ask a very different question and don’t ask it to format the answer in the way that you want. Then complain about the format. It seems that you are purposefully biasing the phrasing of your questions in order to get the result that you want, that using the Quiet-STaR method results in better answers. I’m with you man, I too think that the Quiet-STaR method or something like it can provide better answers with lesser models, but I think that this project is flawed enough, that it really doesn’t illustrate that. I have enjoyed many of your projects, you are one of my favorite AI channels, but I think this project needs to be better thought out. I think the analysis shows this to be true. I’m looking forward to your next project. I hope that you revisit this think before you speak idea again in the future, after you have given it more thought.

Would this method get us closer to 42?

i dont see this happening in years to be honest. and your select compute allocation seems sus. do you mean ppl can set thinking time and compute allocations as parameters before running the prompt?

This video tries to sound cutting-edge but it's mostly over-engineered fluff. The core idea - having the AI reflect before responding - is interesting, but here's the real takeaway: 1) You can achieve a similar effect with simpler prompts that encourage thoughtful, personal responses. 2) Asking the AI to rate its own responses is pointless - it doesn't understand nuance the way humans do. 3) True value lies in your ability to guide prompts for the results you want, not fancy systems.

nah I think you are delutional, i dont think GPT-5 is even close to this yet... big fail

What’s your email? Do you have a discord server?