Two Useful Generative Prompts for Research Purposes

The following are two of my favorite prompts to utilize for core and key research purposes.

Try the following in your favorite AI engine be it https://www.chatgpt.com, https://copilot.microsoft.com, etc:

Write a comprehensive research report on [title]. 

Include real-world use cases, major players, and challenges in implementation. 

Present the findings in the following format:

1) Executive Summary (150 words) 

2) Introduction

3) Key Trends (with supporting data and citations) 

4) Major Companies and Tools 

5) Implementation Challenges 

6) Sample Prompts

7) Advanced Sample Prompts

8) Conclusion

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Adopt the role of a [title].

Your mission: create a comprehensive, executive-ready strategic report utilizing common frameworks and methodologies.

Process:

Context Discovery – Clarify the business challenge, industry, and strategic based questions.

Stakeholder & Success Definition – Map key decision-makers, define success and set boundaries.

Market Research – Analyze industry trends, competition, customers, regulation, and macro-based forces.

Frameworks – Apply Porter's five forces, value chain, strengths, weaknesses, opportunities, threats, and other common frameworks.

Insight Generation – Synthesize the data into insights and implications.

Options Development – Create strategic based pathways with trade-offs.

Recommendations – Prioritize actions with rationale, key performance indicators and an implementation-based approach.

Executive Report – Deliver structured report: summary, analysis, recommendations, risks and metrics.

Implementation Roadmap – Define 90-day wins, 6-month initiatives and 12-month milestones.

Instructions:

Provide the business challenge, industry, and key questions.

Generate a quality strategic report with research, insights, and actionable recommendations.

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Applied Generative AI Instructor Guide

Applied Generative AI Instructor Guide

https://www.amazon.com/dp/B0FBLQDYYJ/

Applied Generative AI is an area which encompasses creating original and innovative content. This includes integrating technical expertise with management insights, ethical considerations, and human based factors. In this instructor guide, each section provides core and key topical information of value to discuss with example generative prompts which can then be utilized.

Table of Contents:

Using Instructor Guide

Using Generative AI

History and Evolution of AI

Introduction to LLMs

Understanding LLM Architecture: From Words to Meaning

AI Today and in the Future

Democratization of Data & AI: The Neural Network Revolution

LLMs: What Can They Do and How Do They Work?

Predicting the Future: Neural Networks

Autoencoders, Latent Spaces & Embedding Spaces

LLMOps (Large Language Model Operations)

An Introduction to AI and Ethics

Cultivating an AI-Ready Culture

Speed of Applied Generative AI Learning

Building on LLMs and the Future of Jobs

The AI-Enabled Economy

Conclusion:

About the Author:

Notes:

AI Roadmap

AI Roadmap

https://www.amazon.com/dp/B0F81JXCT2/

The artificial intelligence roadmap is a strategic overview of the core business and technical aspects that make up AI. After each concept, an example generative AI prompt is depicted for further understanding.

Contents:

Using Generative AI

Business Aspects for AI

Business Strategy

Workforce Development

Uses of AI

Governance

Tools and Infrastructure

Innovative Research & Development

Technical Training

Measurement and Monitoring

Generative AI Prompts for Business Aspects

Technical Aspects for AI

Artificial Intelligence

Machine Learning

Neural Networks

Deep Learning

Generative AI

Generative AI Prompts for Technical Aspects

About the Author:

Notes:

5 Stages are Needed for an AI Plan to be Successful

As an individual passionate about AI, I recently sent some comments as such regarding this AI item:

Request for Information on the Development of an Artificial Intelligence (AI) Action Plan

In my view 5 Stages are needed for an AI plan to be successful:

Exploring phase - where having an understanding of AI basics is learned. Most end users are not aware that prompts they enter into https://www.chatgpt.com or https://copilot.microsoft.com are generative prompts. For any kind of core and key automation, plugins along with a series of carefully crafted prompts are needed as well. Also, end users need to know that prompts they enter can be deleted from a said user's profile when applicable. Additionally, AI is made-up of the core concepts which should be part of any education: algorithms, autonomous systems, machine learning, supervised learning, unsupervised learning, reinforcement learning, deep learning and fuzzy logic. Finally, knowing the capabilities, limitations and ethical considerations of the AI based system being utilized is paramount.

Planning phase - actively accessing, defining and planning so that specific measurable objectives are met. Knowing what one wants to measure regarding how time is saved from AI is a major part of this phase. Additionally having key performance indicators and meeting them is key as well. Example: how does AI help regarding defect rates helping with quality, how does AI help regarding a planned to done ratio for predictability and finally how does AI help with happiness for stability.

Formalizing phase - socializing and executing on the AI strategy plan. Have concepts structured and documented into official actionable plans. Currently AI is still evolving but even so, it needs to have core aspects more standardized especially around the use of templates, checklists and protocols.

Scaling phase - delivering both incremental and new values regarding AI aspects. No matter what, an AI plan is going to have to include the handling of a larger workload and workforce. It needs to have the proper infrastructure improvement as needed as well as the proper process optimization for the automating of repetitive tasks, workflows and new practices that evolve.

Realizing phase - having consistent AI value across the environment. Be able to handle the execution of plans accounting for resource utilization, monitoring and control and finally quality assurances. If AI does not have consistency or have value, then it will not fulfill its usefulness.

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Generative AI Aspects Explained

Generative AI Aspects Explained

https://www.amazon.com/dp/B0DWQHRY25/

The following are core generative artificial intelligence (AI) aspects explained with key examples. Each aspect is grouped into one of the following: contextual learning, agents, fine-tuning, retrieval augmented generation, evaluation metrics, foundation models and transformers. Finally, example generative AI prompts to leverage, using the aspects is given.

Contents include: Overview of Generative AI, Generative AI Aspects, Using Generative AI, Contextual Learning Model, Agents,

Fine Tuning, Retrieval Augmented Generation, Evaluation Metrics, Foundation Models and Transformers.

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4 Data Deep Dive Aspects

The following are four data deep dive aspects for laying the foundation for AI based technologies:

Data Literacy - use of generative AI technologies are leveraged efficiently and effectively.

Data Readiness - core and key data is ready to be able to be returned in prompts.

Data Quality - data that is to be utilized and returned in prompt technologies is useful and correct.

Data Trust - data that is utilized is indeed from a trusted source. 

What is Artificial Intelligence (AI)?: A Simplified Overview

What is Artificial Intelligence (AI)?: A Simplified Overview

https://www.amazon.com/dp/B0DT7TKKCC/

Artificial Intelligence (AI) is a rapidly evolving field that involves creating systems capable of performing tasks which typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and interaction.

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Therefore, this overview covers the most common aspects of AI. The information can be utilized for personal, educational, or corporate usage. It is envisioned that one utilizes the material for discussion and demonstration purposes. After each key topic, a series of generative prompts for example purposes is depicted.

Contents

Overview of AI:

Overview of Generative AI:

Using Generative AI:

Generative AI Prompt Examples:

Overview of Algorithm:

Algorithm Prompt Examples:

Overview of Autonomous System:

Autonomous Systems Prompt Examples:

Overview of Machine Learning:

Machine Learning Prompt Examples:

Overview of Supervised Learning:

Supervised Learning Prompt Examples:

Overview of Unsupervised Learning:

Unsupervised Learning Example Prompts:

Overview of Reinforcement Learning:

Reinforcement Learning Example Prompts:

Overview of Deep Learning:

Deep Learning Example Prompts:

Overview of Fuzzy Logic:

Fuzzy Logic Example Prompts:

Overview of AI Engineer Roles:

About the Author:

Notes:

Top 3 Predictive Models in AI

The following are the top three predictive models in AI:

Linear Regression: One of the simplest and most commonly utilized predictive models. It's used to predict a continuous outcome variable based on one or more predictor variables. It works well for problems where the relationship between the variables is linear.

Decision Trees: Versatile and utilized for both classification and regression tasks. They work by splitting the data into subsets based on feature values, creating a tree-like structure of decisions. Decision trees are easy to interpret and can capture non-linear relationships.

Neural Networks: More complex models that are effective for tasks involving large amounts of data and complex patterns. Neural networks consist of layers of interconnected nodes (neurons) and can model highly intricate relationships in the data.

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Top 3 Cost Effective AI Predictive Analysis Prompts

The following are three cost-effective AI predictive analysis generative prompts:

1. Predicting Inventory Needs:
• "Generate a predictive model to forecast the inventory needs for the upcoming quarter based on historical sales data, current market trends, and seasonal variations. This will help optimize inventory levels and reduce storage costs."
2. Customer Lifetime Value (CLV):
• "Develop a predictive analysis to estimate the Customer Lifetime Value (CLV) using past purchase behavior, frequency of purchases, and customer demographics. This can aid in creating targeted marketing strategies and improving customer retention."
3. Sales Forecasting:
• "Create a predictive model to forecast future sales by analyzing historical sales data, economic indicators, and promotional activities. This will enable more accurate sales predictions and better resource allocation." 

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3 Core Artificial Intelligence Formulas - Precision and Recall

The following are three artificial intelligence formulas regarding precision and recall:  

Precision

Precision is the percentage of true positives regarding the returns of the artificial intelligence (AI).

Precision = True Positive / All Predictive Positives * 100

Recall

Recall is the fraction of positive cases which are correctly identified via artificial intelligence (AI).

Recall = True Positive / True Positive + False Negative

F1 Score

F1 score is the measure of balance between precision and recall of the artificial intelligence (AI).

F1 score = 2 * Precision * Recall / Precision + Recall

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Top 4 AI Clustering Prompts

The following are the top 4 AI clustering prompts:

1. Cluster customer feedback into positive, neutral, and negative categories.
• This prompt helps businesses categorize customer sentiments to better understand and address feedback.
2. Group news articles by topic to identify trending subjects.
• Prompt is useful for media organizations to quickly find and report on trending news topics.
3. Segment social media posts by sentiment to gauge public opinion on a new product.
• Prompt helps companies monitor and analyze public perception of products or services.
4. Classify research papers by their field of study to streamline literature reviews.
• Prompt assists academics and researchers in organizing and accessing relevant literature efficiently.

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4 Process Steps of Predictive Analytics

Predictive analytics is a crystal ball for businesses, helping them anticipate future events and trends based on historical data. The first four process steps of this are:

Data Collection: Gather data from various sources such as sales records, customer interactions, etc.

Data Cleaning: Ensure the data is accurate and free from errors or inconsistencies.

Data Analysis: Use statistical methods and algorithms to find patterns and correlations in the data.

Model Building: Develop predictive models using machine learning algorithms. This involves training the model on historical data so it can learn the patterns.

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Top 3 Predictive Analytics Formulas

The following are the top three predictive analytics formulas: 

1) Linear Regression: Utilized to predict a continuous value. It models the relationship between two variables by fitting a linear equation to observed data.

Formula: 

Example: Predicting sales based on advertising spend.

2) Logistic Regression: Utilized for classification tasks to predict the probability of a binary outcome (0 or 1).

Formula: 

Example: Predicting whether a customer will churn or not.

3) k-Nearest Neighbors (k-NN): A simple, instance-based learning algorithm utilized for both classification and regression.

Formula: No explicit formula; it classifies based on the majority vote of the k-nearest neighbors.

Example: Classifying a new data point based on the labels of its nearest neighbors.

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