Chain-of-thought is a vital technique in prompting generative AI. Turns out that advanced AI does this implicitly. Problems ensue. Here is the inside scoop on what to do.
In today’s column, I examine and provide important recommendations about a crucial prompting technique that is used frequently in generative AI and large language models . The technique is known as chain-of-thought reasoning, which spurs AI to showcase a step-by-step series of statements about how it is solving a given problem. This is extremely valuable so that you can gauge whether the AI is logically getting the solution and potentially discern if it goes awry.
The AI showed me that it did a series of steps, labeled as steps “a”, “b”, “c”, and “d”. The answer of 154 is correct. In this case, the difference is not especially overwhelming. It all depends on the complexity of the problem being solved. If you had a much more complicated problem, the odds are that the combined chain-of-thought will likely reveal additional interesting facets .We are now to the point where I must bring you some bad news. Please prepare yourself accordingly.
A double duty could cost you extra dough and take longer to process since both approaches are happening simultaneously.
Generative AI Large Language Model LLM Chain Of Thought Cot Implicit Explicit Openai Chatgpt O1 O3 Pro Plus GPT-4O Google Gemini Microsoft Copilot Meta Llama Anthropic Claude Double Duty Benefits Downsides Upsides
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