Chris Creates With AI

Episode Title: Diving Prompt First: What is a Prompt? S02 - E12 🧠 What REALLY is a Prompt? Why Does it Matter? We use prompts every day when interacting with AI—but what exactly is a prompt , and why does it matter so much? 🤔 🔥 In this episode, we break it down! 🔥 💡 Chris Tansey’s deep dive into "What is a Prompt?" explains how AI understands and responds to human input. Whether you're just s...

Episode Title: Diving Prompt First: Guide to Real-Life Use Cases for Template-Defined AI Agents S02 - E11 🚀 How AI Agents Are Actually Being Used – Real-World Applications! AI is everywhere , but how do businesses, researchers, and creators actually use it in day-to-day operations? 🤔 💡 Chris Tansey’s latest deep dive into templated AI agent definitions reveals the real-world use cases where AI...

Episode Title: Diving Prompt First: High-Level Guide to Templated AI Agent Creation Process S02 - E10 🎯 Want AI That Actually Works? Follow This 7-Step Blueprint! AI assistants promise to revolutionize our workflow, but too often, they fall short —like a jammed printer right when you need it most. 😩 💡 Enter Chris Tansey’s templated approach to AI agent creation ! This episode breaks down his hi...

Micro-Management as AI Prompting - A Detailed Guide

Managing Contractors and AI - A Parallel Approach

AI Prompting and Human Capital Communication Parallels

Managing Digital and Human Capital Key Parallels

Self-Reflective Leadership Managing Digital & Human Capital

We dive deep into Comparing Managing LLMs with Managing Employees (With Prompt Examples).

We dive into Comparing Managing LLMs with Managing Employees.

We discover how Goal Oriented Frameworks our useful for successful chatbot interactions

We compare communicating with Chatbots to communicating with Rain Man.

We discuss a technique called self-consistency which enhances the reasoning capabilities of large language models (LLMs). This technique involves prompting an LLM to generate multiple reasoning paths for a question and then selecting the most consistent answer among these paths. This method improves the accuracy and reliability of LLMs, particularly for tasks requiring complex reasoning, such as a...

ART addresses the limitations of traditional Chain-of-Thought (CoT) prompting by enabling LLMs to decompose tasks into multiple steps and utilize external resources, ultimately improving their performance on tasks requiring reasoning and complex problem-solving.

ReAct (Reason + Act) is a prompting technique that enhances the capabilities of Large Language Models (LLMs) by enabling them to reason, plan, and interact with external tools and data sources . This technique aims to overcome the limitations of traditional LLMs, which are restricted to their training data, leading to more accurate, reliable, and sophisticated applications.

A technique that enhances large language models (LLMs) by integrating them with external knowledge sources.

Large Language Models Are Human-Level Prompt Engineers.

Discover the Next Level of AI Interaction

AI That Thinks Like We Do:

Unraveling AI’s Thought Process

Building Complex Ideas, One Prompt at a Time

Maximizing Context with Targeted Examples

Mastering the Art of Zero-Shot Responses

A comprehensive guide to prompt engineering, a crucial aspect of working with large language models (LLMs)