Prompt Engineering Guide: 2026 Edition (Steal My System)

The podcast introduces a prompt engineering framework designed for effective use across various AI models. It emphasizes the importance of specificity in defining the role and task for the AI, tailoring the context to suit different models like GPT-5, Claude, and Perplexity. The discussion covers the strategic use of examples, output formatting, and constraints to guide AI responses, along with advanced techniques such as chain of thought and chain of verification to refine the AI's reasoning. Reverse prompting is presented as a method where the AI designs its own prompts. The podcast also addresses context engineering, clarifying that it enhances rather than replaces prompt engineering by ensuring the AI has access to the right information.

Outlines

Part 1: Framework Introduction

Part 2: Core Components of Prompting

Part 3: Advanced Techniques, Guardrails

Part 4: Future Trends

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Ali H. Salem

Part 1: Framework Introduction

Introduction to a Reusable Prompt Engineering Framework for AI Models

Prompt Engineering Framework: Structure and XML Sandwich Technique

Part 2: Core Components of Prompting

Defining the Role in Prompt Engineering: Specificity and Persona Maintenance

Task Definition: Action Verbs, Objectives, and Model-Specific Considerations

Context Optimization: Model-Specific Strategies for Providing Information

Leveraging Examples: Few-Shotting and Model-Specific Recommendations

Output Specification: Surgical Formatting for Desired Responses

Part 3: Advanced Techniques, Guardrails

Constraints and Instructions: Establishing Guardrails and Advanced Techniques

Chain of Verification: AI Double-Checking for Enhanced Reasoning

Reverse Prompting: Letting AI Design Optimal Prompts for Tasks

Part 4: Future Trends

Context Engineering: Enhancing AI with Relevant Information and Future Trends

Prompt Engineering Guide: 2026 Edition (Steal My System)

Ali H. Salem

Part 1: Framework Introduction

00:00Introduction to a Reusable Prompt Engineering Framework for AI Models

Introduction to a Reusable Prompt Engineering Framework for AI Models

00:58Prompt Engineering Framework: Structure and XML Sandwich Technique

Prompt Engineering Framework: Structure and XML Sandwich Technique

Part 2: Core Components of Prompting

01:39Defining the Role in Prompt Engineering: Specificity and Persona Maintenance

Defining the Role in Prompt Engineering: Specificity and Persona Maintenance

02:30Task Definition: Action Verbs, Objectives, and Model-Specific Considerations

Task Definition: Action Verbs, Objectives, and Model-Specific Considerations

03:54Context Optimization: Model-Specific Strategies for Providing Information

Context Optimization: Model-Specific Strategies for Providing Information

04:49Leveraging Examples: Few-Shotting and Model-Specific Recommendations

Leveraging Examples: Few-Shotting and Model-Specific Recommendations

06:18Output Specification: Surgical Formatting for Desired Responses

Output Specification: Surgical Formatting for Desired Responses

Part 3: Advanced Techniques, Guardrails

07:13Constraints and Instructions: Establishing Guardrails and Advanced Techniques

Constraints and Instructions: Establishing Guardrails and Advanced Techniques

08:50Chain of Verification: AI Double-Checking for Enhanced Reasoning

Chain of Verification: AI Double-Checking for Enhanced Reasoning

10:26Reverse Prompting: Letting AI Design Optimal Prompts for Tasks

Reverse Prompting: Letting AI Design Optimal Prompts for Tasks

Part 4: Future Trends

11:33Context Engineering: Enhancing AI with Relevant Information and Future Trends

Context Engineering: Enhancing AI with Relevant Information and Future Trends