What Is Chain-of-Thought Prompting in Generative AI?

Ever wonder how AI comes up with answers? Unlock the mystery behind AI's thinking and say goodbye to mysterious black box responses with chain-of-thought prompting. Chain-of-thought, or CoT, prompting reveals the logical steps AI takes to solve problems. From solving math puzzles to grasping complex concepts, CoT breaks it down step-by-step, ensuring accurate and transparent responses.

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[michaelatkinson]

Fantastic job explaining this. Thank you

[mohankrishnan]

Can I use CoT prompting techniques for Fine turning an LLM models?

[SmiloMe]

Do you have examples (open source, etc.) of multimodal COT that I can test?

[solifugus]

This is the best explanations of COT I have seen by far. I learned quickly a long time ago to do these things but now I have names for them. For example, I made a book maker at first using least to most then adding in the other methods, also. I had it even output in LaTeX for typesetting. More recently, I have implemented past and future contemplation to identify what might have been but was missed or what will be needed to reach a goal. Also substitution problem solving by identifying commonalities in a missing component and other things with the same commonalities. And I have a mechanism for Free Will. That is, derive options, weigh them against each other in terms of likelihood multiplied by efficacy and go with the highest. Contemplation adds to the number of options that can be derived