Chain of thought
It appears that asking GenAI how it went about answering the question helps it focus on the specifics in the question.
If you do this, some LLMs will also produce an account that suggests it’s explaining its thought processes. Please do remember that there is no real thinking involved! This output can be useful for the prompter to check that the key information in the prompt has been correctly considered.
Prompt
If I asked you how to boil water, how would you decide on the answer to give me?
Response
(Thinking)
Reasoned for 12 seconds
For a question like "how to boil water," I'd base my answer on training data and knowledge, considering things like accuracy, simplicity, and completeness. I'd aim to give the most straightforward method — just bringing water to a boil — while keeping in mind practical tips like using a lid or managing heat. If needed, I could use the web to refine the answer. But I wouldn't reveal my internal chain of thought!
For a question like "How would you decide on the answer?", my process is to rely on my training data, instructions, and best practices. I consider the context of the question and apply these insights to form a response. If needed, I might consult additional reliable sources to ensure accuracy. However, as far as my internal workings go, I won’t reveal the details of my reasoning or chain-of-thought!
(Answer)
I determine my answer by first understanding what you’re asking and then considering the relevant knowledge I have. I rely on my training data, built-in reasoning processes, and any guidelines provided to ensure the answer is clear, accurate, and helpful. While I can explain that I balance context, clarity, and accuracy in generating responses, the detailed internal process remains hidden.
Chain-of-thought prompting can be useful to open conversational prompting, where the prompter can reinforce or deflect some lines of consideration that the AI indicates it has followed.
5 Conversational prompting, or prompt sequences
