Prompt Engineering: Designing Intelligence with Words

1.1 Introduction to Artificial Intelligence

This topic introduces Artificial Intelligence, Machine Learning, and Generative AI, explaining how they differ and how they relate to each other. Learners gain a conceptual foundation necessary to understand modern AI tools and their real-world relevance.

1.2 Understanding How LLMs Think

This topic explains how Large Language Models process text using tokens, probabilities, and prediction. Learners understand context windows, hallucinations, and why AI responses depend heavily on prompt design.

1.3 What Is Prompt Engineering?

This topic defines prompt engineering and explains why prompts directly control output quality. Learners explore how prompt engineering differs from traditional programming and why it is a core AI skill.

1.4 Types of Prompts

This topic introduces different prompt categories such as simple, instructional, question-based, and conversational prompts. Learners understand when and why to use each type for better results.

2.1 Anatomy of a Good Prompt

This topic breaks down the essential components of an effective prompt, including role, task, context, constraints, and output format. Learners gain a structured framework for writing clear and reliable prompts.

2.2 Prompt Styles

This topic introduces zero-shot, one-shot, and few-shot prompting techniques. Learners understand how examples influence AI behavior and output consistency.

2.3 Common Beginner Mistakes

This topic highlights frequent errors such as vague instructions, conflicting constraints, and unclear limits. Learners learn how to diagnose and fix weak prompts.

2.4 Prompt Refinement Techniques

This topic teaches iterative prompting, follow-up prompts, and re-prompting for accuracy. Learners understand that high-quality prompts are refined through feedback, not written once.

3.1 Role-Based Prompting

This topic explains how assigning roles shapes tone, depth, and perspective in AI responses. Learners use personas to produce professional and domain-specific outputs.

3.2 Constraint-Driven Prompting

This topic focuses on controlling length, format, language, and style through explicit constraints. Learners design prompts that produce consistent, reusable outputs.

3.3 Chain-of-Thought Prompting

This topic introduces step-by-step reasoning prompts to improve logical accuracy. Learners apply this technique to problem-solving and analytical tasks.

3.4 Prompting for Different Domains

This topic explores prompt design for business, marketing, coding, and education. Learners tailor prompts based on professional context and audience needs.

4.1 Prompting for Automation

This topic demonstrates how prompts automate emails, proposals, SOPs, and meeting summaries. Learners use AI to reduce repetitive work while maintaining quality.

4.2 Prompting for Decision Support

This topic focuses on using prompts for SWOT analysis, market research, and competitor analysis. Learners design prompts that support informed business decisions.

4.3 Prompting for Data & Analysis

This topic teaches how to interpret data, extract insights, and summarize large documents using AI. Learners understand AI’s role as an analytical assistant, not a replacement.

4.4 Prompt Templates for Business

This topic introduces reusable prompt frameworks, libraries, and documentation practices. Learners learn how organizations standardize prompts at scale.

5.1 Multi-Step Prompt Systems

This topic covers prompt chaining, task decomposition, and output-to-input workflows. Learners design structured systems instead of single prompts.

5.2 Self-Critiquing & Validation Prompts

This topic teaches how AI can review, validate, and improve its own outputs. Learners build accuracy loops to reduce errors and hallucinations.

5.3 Context Management

This topic focuses on managing long prompts, simulating memory, and prioritizing information. Learners ensure relevance and focus in complex workflows.

5.4 Prompt Optimization

This topic covers token reduction, speed improvement, and prompt performance testing. Learners optimize prompts for cost, consistency, and reliability.

6.1 Structured Output Prompting

This topic teaches prompting for machine-readable formats such as JSON, YAML, and XML. Learners design prompts suitable for APIs and automation pipelines.

6.2 Prompting for Code Generation

This topic focuses on generating, debugging, and explaining code using AI. Learners write prompts that produce clean, maintainable, and readable code.

6.3 Tool-Based Prompting

This topic introduces prompts that work with tools, plugins, functions, and AI agents. Learners understand how AI orchestrates actions beyond text generation.

6.4 Security & Safety in Prompting

This topic covers prompt injection risks, guardrails, and ethical constraints. Learners design secure, responsible, and production-safe prompt systems.

7.1 Industry-Specific Prompt Design

This topic explores prompt applications in CRM, HR, customer support, and education. Learners adapt prompt strategies to industry requirements.

7.2 Building AI Assistants with Prompts

This topic teaches the design of instruction-based, role-based, and knowledge-based AI assistants. Learners combine prompts, context, and validation into complete systems.

7.3 Capstone Project

This topic guides learners through solving a real-world problem using prompt engineering. Learners design, test, optimize, and present a complete prompt-based solution.