Prompt Engineer Course

Lesson 1 • Key Terminology and Mental Models

Defines core vocabulary—temperature, top-p, context window, hallucination—used throughout the course. Provides shared language for precise prompt analysis.

Lesson 2 • The Role of Prompts in Model Output

Explains how input text shapes model responses through context, framing, and instruction clarity. Connects prompt structure directly to output quality.

Lesson 3 • Prompt Engineering as a Discipline

Positions prompt engineering within AI workflows and professional roles. Students understand scope, limitations, and the iterative nature of the craft.

Lesson 1 • Output Specification Techniques

Covers format directives, length constraints, and structural templates for controlling model output shape. Ensures outputs are immediately usable in downstream workflows.

Lesson 2 • Core Prompt Components

Breaks down instruction, context, input data, and output indicator as the four primary building blocks. Teaches how each component contributes to response accuracy.

Lesson 3 • Writing Clear Instructions

Focuses on verb choice, scope definition, and constraint specification within the instruction component. Directly reduces ambiguity and off-target responses.

Lesson 4 • Diagnosing Weak Prompts

Introduces a systematic checklist for identifying vague, conflicting, or incomplete prompt elements. Builds the diagnostic habit essential for iterative improvement.

Lesson 1 • Chain-of-Thought Prompting

Introduces step-by-step reasoning elicitation to improve accuracy on complex tasks. Connects reasoning transparency to error detection and output reliability.

Lesson 2 • Selecting the Right Technique

Provides a decision framework for matching prompting technique to task type, complexity, and resource constraints. Synthesizes the section into applied judgment.

Lesson 3 • Zero-Shot Prompting

Teaches how to elicit accurate responses without examples by maximizing instruction precision. Establishes the baseline technique from which all other methods build.

Lesson 4 • Instruction Tuning and Prompt Chaining

Teaches how to sequence multiple prompts to handle tasks too complex for a single call. Builds pipeline thinking essential for advanced applications.

Lesson 1 • Self-Critique and Iterative Refinement

Teaches prompts that instruct the model to review and improve its own output. Builds a closed-loop quality mechanism within a single or multi-turn session.

Lesson 2 • Self-Consistency and Majority Voting

Covers generating multiple independent responses and aggregating them for higher accuracy. Connects statistical sampling to reliability improvement in high-stakes tasks.

Lesson 3 • Handling Ambiguity and Edge Cases

Equips students to design prompts that gracefully manage unclear inputs and unexpected scenarios. Reduces brittle prompt behavior in production environments.

Lesson 4 • Tree-of-Thought and Branching Reasoning

Extends chain-of-thought into parallel reasoning paths for problems with multiple valid approaches. Teaches when branching outperforms linear reasoning chains.

Lesson 1 • Conversational and Customer-Facing Prompts

Designs prompts for chatbots, virtual assistants, and support agents that maintain context and tone. Addresses user experience and escalation handling.

Lesson 2 • Content Creation and Copywriting Prompts

Covers tone calibration, audience targeting, and format control for marketing, editorial, and creative content. Connects prompt design to brand and communication goals.

Lesson 3 • Research and Knowledge Synthesis Prompts

Teaches prompts that aggregate, compare, and synthesize information from multiple sources or perspectives. Builds research-grade output quality through structured inquiry.

Lesson 4 • Data Analysis and Summarization Prompts

Teaches structured prompts for extracting insights, summarizing documents, and formatting analytical outputs. Builds prompts that integrate into data workflows.

Lesson 1 • Multi-Turn Conversation Design

Covers turn-by-turn prompt design that maintains coherence, tracks user intent, and adapts dynamically. Builds the scaffolding for reliable conversational AI products.

Lesson 2 • Context Window Management

Teaches strategies for fitting relevant information within token limits across long conversations. Directly addresses memory loss and context degradation in extended sessions.

Lesson 3 • Testing Multi-Turn Prompt Systems

Introduces simulation, red-teaming, and regression testing for conversational prompt systems. Ensures reliability before deployment in user-facing environments.

Lesson 4 • System Prompt Architecture

Covers the structure, scope, and authority of system prompts relative to user turns. Establishes how system-level instructions govern all downstream interactions.

Lesson 1 • Defining Prompt Success Metrics

Covers accuracy, relevance, coherence, and task-completion rate as measurable prompt quality dimensions. Connects metric selection to specific use-case requirements.

Lesson 2 • Iterative Prompt Optimization Workflow

Establishes a structured cycle of hypothesis, test, analyze, and revise for continuous prompt improvement. Integrates evaluation findings into actionable redesign steps.

Lesson 3 • A/B Testing and Prompt Variants

Covers controlled comparison of prompt variants to identify statistically meaningful performance differences. Builds rigorous experimentation habits for prompt optimization.

Lesson 4 • Manual and Automated Evaluation Methods

Teaches human review rubrics alongside automated scoring tools for scalable prompt assessment. Balances evaluation depth with operational efficiency.

Lesson 1 • Scaling Prompt Engineering Across Teams

Covers governance models, style guides, and collaboration workflows for multi-team prompt development. Builds the organizational capability for enterprise-scale prompt operations.

Lesson 2 • Safety, Bias, and Responsible Deployment

Addresses bias auditing, harmful output prevention, and transparency requirements for production prompts. Connects ethical design to organizational risk management.

Lesson 3 • Integrating Prompts into AI Pipelines

Teaches how prompts connect to APIs, orchestration frameworks, and data pipelines in production. Bridges prompt design with software engineering deployment practices.

Lesson 4 • Monitoring Prompt Performance in Production

Covers logging, alerting, and drift detection for live prompt systems. Ensures prompt quality is maintained as user behavior and model updates evolve.