Ai (Llm) Prompting Frameworks For Problem-Solving

Master AI (LLM) Prompt Frameworks. Effectively Apply Problem-Solving Prompts to Unlock the Full Potential of AI Systems.

What you'll learn

Implement problem-solving prompting frameworks to real-life scenarios

Mastery of various LLM prompt frameworks for problem-solving

Have access to over 300 AI prompts for problem-solving using various prompt frameworks

Apply Tree of Thoughts (ToT) Prompting technique

Leverage LLMs for virtually any text-based task through prompting frameworks.

Understand the art of persona development and apply Persona to AI prompts

Understand the strategy of System Prompt Design

Leverage LLM prompt tones in creating Clear-contextual prompting

What are LLMs? What is a prompt? What is AI prompt engineering?

Understanding how AI (Large language models) interpret instructions and the specific use cases of some LLMs

Requirements

Developing LLM prompting frameworks is fundamentally about precise communication, so students should have the ability to express ideas clearly and concisely in writing.

Students need reliable internet connectivity and access to a current web browser (Chrome, Firefox, Safari, or Edge) to interact with AI platforms, complete hands-on exercises, and access course materials and resources.

Students should possess intellectual curiosity about AI (LLM) technology, willingness to experiment with new tools and techniques, and patience for iterative learning processes as prompt engineering often requires trial and refinement.

Having a Gmail account will be needed to easily create accounts in the LLMs

Description

AI (Large language models (LLMs)) Prompting Frameworks For Problem-Solving: Master LLM Prompt Frameworks. Effectively Apply Problem-Solving Prompts to Unlock the Full Potential of LLMs. Note: This course does not involve coding or image/video generation .Welcome to the most comprehensive LLM Prompting framework course available today, designed to transform you from a complete beginner into a skilled prompting expert capable of maximizing the potential of any LLMs. In our rapidly evolving digital landscape, the ability to effectively communicate with artificial intelligence has become as crucial as traditional programming skills. This course will teach you the art of prompt frameworks– the strategic craft of designing, optimizing, and implementing prompts that generate precise, valuable, and reliable outputs from AI (Large language models (LLMs)) models.Prompt engineering represents the intersection of human creativity and artificial intelligence capability. As LLMs like ChatGPT, Claude, Grok, Meta AI, Gemini and other large language models become increasingly sophisticated, the quality of your interactions with these systems directly determines the value you can extract from them. Whether you're a business professional seeking to curate workflows, a solution-provider looking to enhance productivity, or anyone building AI-powered solutions, then mastering prompt frameworks will give you a significant competitive advantage.We will look also into foundational areas like:- What is AI?-What are Large Language Models (LLMs)?- How AI (LLMs) Models Process Instructions- What is a prompt?- What is AI (LLM) prompt engineering?This course takes you on a comprehensive journey through the fundamentals of AI communication, starting with basic prompt construction and advancing to sophisticated techniques used by AI experts. We'll explore the psychological and technical principles that make prompts effective, diving deep into the architecture of large language models to understand how they process and respond to different types of input.Throughout the course, you'll master essential prompt techniques including zero-shot and few-shot prompting, chain-of-thought reasoning, persona development, role-playing scenarios, Tree of Thoughts (ToT) Prompting and more. You'll discover how to construct prompts that consistently produce high-quality outputs.What sets this course apart is its practical, hands-on approach combined with deep theoretical understanding. Every framework concept is immediately applied through real-world exercises spanning multiple industries and use cases. You'll work with actual LLMs, building a comprehensive prompt library that you can use immediately in your professional and personal projects. You'll learn to leverage AI for virtually any text-based task.You'll understand the limitations and capabilities of different LLMs and apply prompt frameworks in various contexts.Our structured learning approach ensures progressive skill development through carefully designed modules, each building upon previous knowledge while introducing new concepts. Interactive exercises and real-world case studies provide multiple opportunities to practice and refine your skills. By the end of this course, you'll possess the knowledge and skills to design sophisticated prompt-based applications, optimize AI interactions for specific business objectives, and stay ahead of rapidly evolving AI capabilities. You'll understand how to evaluate and compare different AI models, select appropriate tools for specific tasks, and integrate AI seamlessly into existing workflows and processes.Whether you're looking to enhance your current role, transition into AI-focused career paths, or simply understand how to harness AI more effectively in your daily work, this course provides the comprehensive foundation you need to succeed in the age of artificial intelligence.

Overview

Section 1: Introduction

Lecture 1 Introduction - part 1

Lecture 2 Introduction - part 2

Lecture 3 Intro - 1

Lecture 4 Intro - 2

Section 2: Artificial Intelligence/ LLM [Theoretical Foundation]

Lecture 5 An introduction

Lecture 6 What is AI?

Lecture 7 What are Large Language Models?

Section 3: LLMs we will be exploring

Lecture 8 Identify the different AI (LLMs) Models we will be exploring

Lecture 9 Top performing LLMs according to research

Lecture 10 What are the limitations of LLMs?

Lecture 11 What LLMs cannot do

Lecture 12 If you want to have the knowledge about how LLMs process instructions

Section 4: What is a prompt? And Effective prompting techniques [Theoretical Foundation]

Lecture 13 What is a prompt?

Lecture 14 What is AI (LLM) prompt engineering?

Lecture 15 Effective prompt techniques

Section 5: Zero-shot prompting

Lecture 16 Zero-shot prompting

Section 6: Clear-contextual prompting with TONE

Lecture 17 Understand the basis for tone in prompting

Lecture 18 Why LLM Prompt Tone Matters

Lecture 19 Categories and Types of tones in LLM prompts

Lecture 20 Providing context and tone to your prompt - part 1

Lecture 21 Providing context and tone to your prompt - part 2

Lecture 22 Assignment

Section 7: LLM Prompts with Writing styles

Lecture 23 Simple Prompts with Writing styles

Lecture 24 Prompts with Writing styles - part 1

Lecture 25 Prompts with Writing styles - part 2

Lecture 26 Prompts with Writing styles - part 3

Section 8: LLM Prompt Frameworks: One-shot prompting

Lecture 27 Understanding One-shot prompting

Lecture 28 Exploring examples of prompts using One-shot - part 1

Lecture 29 Exploring examples of prompts using One-shot - part 2

Lecture 30 Assignment

Section 9: LLM Prompt Frameworks: Few-Shot Framework

Lecture 31 Understanding Few-Shot Framework

Lecture 32 Exploring examples of prompts using Few-shot - part 1

Lecture 33 Exploring examples of prompts using Few-shot - part 2

Lecture 34 Assignment

Section 10: LLM Prompt Frameworks: Chain-of-Thought (CoT) Framework

Lecture 35 Understanding Chain-of-Thought (CoT) Framework

Lecture 36 Exploring examples of prompts using CoT prompting - part 1

Lecture 37 Exploring examples of prompts using CoT prompting - part 2

Section 11: LLM Prompt Frameworks: CTAF Framework

Lecture 38 Understanding the CTAF prompting

Lecture 39 Exploring examples of prompts using CTAF prompting - part 1

Lecture 40 Exploring examples of prompts using CTAF prompting - part 2

Section 12: LLM Prompt Frameworks: SCAMPER Framework

Lecture 41 Understanding the SCAMPER framework

Lecture 42 Key features of SCAMPER Prompting

Lecture 43 Detailed Breakdown of Each SCAMPER Element - S

Lecture 44 Detailed Breakdown of Each SCAMPER Element - C

Lecture 45 Detailed Breakdown of Each SCAMPER Element - A

Lecture 46 Detailed Breakdown of Each SCAMPER Element - M

Lecture 47 Detailed Breakdown of Each SCAMPER Element - P

Lecture 48 Detailed Breakdown of Each SCAMPER Element - E

Lecture 49 Detailed Breakdown of Each SCAMPER Element - R

Lecture 50 Using more than one element of the SCAMPER method

Section 13: LLM Prompt Frameworks: Thinking Hats Framework

Lecture 51 Understanding the Six Thinking Hats Framework

Lecture 52 The Six Thinking Hats Framework Components - Part 1

Lecture 53 The Six Thinking Hats Framework Components - Part 2

Lecture 54 The Six Thinking Hats Framework Components - Part 3

Lecture 55 Exploring The Six Thinking Hats Framework prompts

Section 14: LLM Prompt Frameworks: System Prompt Design

Lecture 56 Understanding the System Prompt Design

Lecture 57 Exploring System Prompt Design prompts - part 1

Lecture 58 Exploring System Prompt Design prompts - part 2

Section 15: LLM Prompt Frameworks: RTFD Framework

Lecture 59 Understanding the RTFD Framework

Lecture 60 Exploring RTFD Prompting - part 1

Lecture 61 Exploring RTFD Prompting - part 2

Section 16: LLM Prompt Frameworks: RISEN Framework

Lecture 62 Understanding the RISEN Framework

Lecture 63 Exploring RISEN prompts (Narrowing) - Part 1

Lecture 64 Exploring RISEN prompts (Narrowing) - Part 2

Lecture 65 Exploring RISEN prompts (Novelty) - Part 1

Lecture 66 Exploring RISEN prompts (Novelty) - Part 2

Section 17: LLM Prompt Frameworks: R/B Team Analysis Framework

Lecture 67 Understanding the R/B Team Framework

Lecture 68 R /B Team Analysis Components - Red-Blue Team

Lecture 69 R/B Team Analysis Components - Integration Analysis

Lecture 70 Exploring R/B Team Analysis prompts - Part 1

Lecture 71 Exploring R/B Team Analysis prompts - Part 2

Section 18: LLM Prompt Frameworks: Persona Development

Lecture 72 Understanding Persona Development

Lecture 73 Benefits of Persona Development

Lecture 74 Exploring Persona Development prompts - part 1

Lecture 75 Exploring Persona Development prompts - part 2

Section 19: LLM Prompt Frameworks: Tree of Thoughts (ToT) Prompting

Lecture 76 Understanding Tree of Thoughts (ToT) Prompting

Lecture 77 Core Framework Architecture of ToT Prompting

Lecture 78 Exploring ToT prompts to understand framework flow - Part 1

Lecture 79 Exploring ToT prompts to understand framework flow - Part 2

Lecture 80 Exploring ToT prompts to understand framework flow - Part 3

Section 20: How LLMs Process Instructions [Theoretical Foundation] - optional

Lecture 81 Introduction

Lecture 82 Tokenization

Lecture 83 Vector embedding

Lecture 84 Types of vector embeddings

Lecture 85 Why Vector Embeddings Matter - Part 1

Lecture 86 Why Vector Embeddings Matter - Part 2

Lecture 87 Cosine Similarity

Lecture 88 Contextual understanding

Lecture 89 Transforming into Output

Lecture 90 The quality of instruction processing depends on prompt clarity

Lecture 91 Specific use cases optimization for ChatGPT and Grok

Lecture 92 Specific use cases optimization for Claude and Gemini

Lecture 93 Specific use cases optimization for Perplexity and Meta

Lecture 94 Specific use cases optimization for Qwen and Copilot

Lecture 95 Specific use cases optimization for Mistral and Poe

Lecture 96 Specific use cases optimization for You.com and DeepSeek

Lecture 97 Practical Implications of specific use cases for Your Prompting

For anyone building solutions and wanting to master prompting frameworks that will give you a significant competitive advantage,Solution-provider looking to enhance productivity and solve problems effectively