https://github.com/NirDiamant/Prompt_Engineering

I’m looking to harness the collective smarts of our Data Alchemy community. Who is interested in taking on the challenge of evolving a Large Language Model to process queries with the insight and depth of an expert? ## The problem with current LLMs The more you integrate LLMs in your workflows, the clearer their shortcomings become. Let’s say you request a business plan for “an AI-driven e-commerce site”. It will jump straight into a carbon-copy template business buzzwords – Problem Definition, Unique Value Proposition, Early Adopters – without context or insights. Why doesn’t the LLM pause and wonder: what does “AI-driven” mean to you? What stage of your business story are you in? It's the nuance, the back-and-forth of understanding that's missing—direct prompts often lead to a one-way street of uninspired affirmations. In my talks with business people, a recurring theme is their need for an LLM that doesn’t churn out cookie-cutter answers, but crafts them with the considered questions of an expert analyst. Wouldn’t it be great if we were the ones to offer more useful LLMs to our customers? ## A new mental model In May of this year, academics proposed a mental model where the AI imagines a team of experts discussing the problem, each representing a different point of view. They propose their ideas step by step to the group, up until the point that they realize they are wrong, which is when they leave: “3 experts are discussing the question with a panel discussion, trying to solve it step by step, and make sure the result is correct and avoid penalty.” What is it about this prompt that triggers AIs into more productive thinking patterns? This 'panel' construct reshapes the pattern, emphasizing critical engagement over rote response. Instead of locking onto the most expected answer, the AI is coaxed into a more layered and intricate mode of thinking. But does it do so structurally, for the rest of the conversation?

I see that hallucination is on of the major issues that everybody comes across using Gen AI. A human oversight is needed to no befall this but are there ways to mitigate this?

Hey guys, I am beginner who has been playing with n8n to learn agents so far. Please could you explain me if n8n is comparable to Data Alchemy’s system? I want to see if I invest my time learning Python to work with Data Alchemy’s System. Thank you in advance.

Hi Community, I’m curious to connect with others working at the intersection of business and machine learning. Are any of you currently using Snowflake for RAG (Retrieval-Augmented Generation) or ML model creation? Additionally, I’d love to hear from anyone with experience building ML workflows using Snowflake’s Model Context Protocol (MCP) and Cortex Analyst. What does your setup look like, and what lessons have you learned? Looking forward to exchanging ideas and learning from your experiences!