AI Prompt Engineer Course

Lesson 1 • Anatomy of a Prompt

Breaks a prompt into its functional components: instruction, context, input data, and output format. Teaches students to identify and label each part.

Lesson 2 • How Large Language Models Work

Explains token prediction, training data, and model architecture at a conceptual level. Establishes why prompt wording directly affects model output.

Lesson 3 • Core Vocabulary and Mental Models

Introduces essential terminology used throughout the course. Provides mental models that simplify reasoning about model behavior.

Lesson 1 • Controlling Output Format

Instructs models to return specific formats such as JSON, tables, or prose paragraphs. Enables downstream processing and integration with other tools.

Lesson 2 • Writing Clear Instructions

Covers specificity, active voice, and unambiguous verb choice in prompt instructions. Directly reduces vague or off-target model responses.

Lesson 3 • Iterative Prompt Refinement

Establishes a structured loop for diagnosing and improving underperforming prompts. Builds the habit of evidence-based iteration rather than guesswork.

Lesson 1 • Decomposition and Chaining Patterns

Breaks complex tasks into sequential sub-prompts and chains their outputs. Enables reliable handling of tasks too large for a single prompt.

Lesson 2 • Chain-of-Thought Prompting

Elicits step-by-step reasoning to improve accuracy on complex tasks. Demonstrates when and how to trigger reasoning chains effectively.

Lesson 3 • Zero-Shot and Few-Shot Prompting

Contrasts prompting with no examples versus providing labeled demonstrations. Teaches when each approach maximizes accuracy and efficiency.

Lesson 4 • Role and Persona Prompting

Assigns expert personas to shape tone, vocabulary, and reasoning style. Connects persona design to task-specific output quality.

Lesson 1 • Retrieval-Augmented Prompting Basics

Introduces injecting retrieved external documents into prompts to ground responses. Connects context management to knowledge-base-driven applications.

Lesson 2 • Understanding Context Windows

Explains context window size, token counting, and how models attend to context. Grounds all subsequent memory and retrieval strategies.

Lesson 3 • Designing Multi-Turn Conversations

Structures dialogue flows that maintain coherence across many exchanges. Applies directly to chatbot and assistant product development.

Lesson 1 • Agentic Prompting and Tool Use

Designs prompts that enable models to plan, call external tools, and act autonomously. Bridges prompt engineering with AI agent system design.

Lesson 2 • Prompt Injection and Security Awareness

Identifies adversarial prompt injection attacks and designs defenses against them. Protects production applications from manipulation and data leakage.

Lesson 3 • Tree-of-Thought Prompting

Structures reasoning as a branching search tree to explore multiple solution paths. Enables systematic problem-solving beyond linear chain-of-thought.

Lesson 4 • Self-Consistency and Majority Voting

Generates multiple independent reasoning paths and aggregates answers by majority vote. Improves reliability on tasks with high variance outputs.

Lesson 1 • Building an Evaluation Pipeline

Assembles test sets, scoring scripts, and reporting dashboards into a repeatable pipeline. Enables continuous monitoring of prompt quality over time.

Lesson 2 • Defining Evaluation Criteria

Establishes measurable quality dimensions such as accuracy, relevance, and format compliance. Converts subjective quality judgments into objective metrics.

Lesson 3 • Human Evaluation Methods

Designs rubrics and rating scales for human judges to assess model outputs. Provides ground-truth labels for automated evaluation calibration.

Lesson 1 • Persona and Brand Voice Design

Embeds consistent brand identity and communication style into system prompts. Ensures every model response aligns with organizational voice standards.

Lesson 2 • Versioning and Maintaining System Prompts

Applies software-style version control to system prompt management. Enables safe iteration and rollback in live applications.

Lesson 3 • System Prompt Architecture

Defines the structure and placement of system-level instructions relative to user turns. Establishes the foundation for all application-layer prompt design.

Lesson 1 • Latency and Cost Optimization

Reduces token usage and response time through prompt compression and model selection. Enables cost-effective scaling without sacrificing output quality.

Lesson 2 • Prompt Governance and Documentation

Establishes organizational standards for prompt ownership, review, and documentation. Ensures maintainability and accountability across teams and projects.

Lesson 3 • Monitoring and Observability

Instruments production prompt pipelines with logging, tracing, and alerting. Enables rapid detection and diagnosis of quality regressions in live systems.