Module 2 talks about reflection in agentic AI. The examples all start with a very simple prompt and then it uses reflection prompt, which has a detailed set of criteria, to let the LLM iteratively refine on previous draft. Also, a reasoning model is better at debugging or reviewing.
My question is, is fitting all the reflection prompts (which contains detailed instructions) in the original LLM and using a powerful reasoning model upfront equivalent to multi-step reflection? Maybe it could produce a plausible result at first round.
Just my two cents, I’m convinced reflection can help with coding as LLM can’t always produce 100% executable code. But for other more text bases tasks like writing an email, creating domains, etc. Can’t a properly written prompt and a powerful model nail it at first time?
More prompts mean more outputs from the LLM, and the chances of steering in the right direction are better. If the model is well-trained and has an efficient architecture, it might return the right result at first prompt, and this is where LLMs are moving to as time passes.