Prompt Engineering for AI Applications Course

Lesson 1 • The Role of Training Data

Explains how pretraining data shapes model knowledge, biases, and knowledge cutoffs. Connects data origins to predictable model strengths and blind spots.

Lesson 2 • Model Inputs and Outputs

Defines the anatomy of a model call: system messages, user turns, and assistant responses. Grounds learners in the interface structure before writing any prompts.

Lesson 3 • Model Types and Their Capabilities

Distinguishes instruction-tuned, base, and chat models and their intended use cases. Helps learners select the right model type for a given task.

Lesson 1 • Anatomy of an Effective Prompt

Breaks down instruction, context, input data, and output format as the four core prompt elements. Learners practice assembling each component deliberately.

Lesson 2 • Writing Clear Instructions

Teaches imperative phrasing, action verbs, and constraint statements that reduce ambiguity. Directly improves first-pass output quality across all task types.

Lesson 3 • Persona and Tone Specification

Demonstrates how role assignment and tone descriptors shift model voice and expertise level. Learners calibrate outputs for specific audiences and communication styles.

Lesson 4 • Common Prompt Anti-Patterns

Identifies overloaded instructions, contradictory constraints, and under-specified tasks that degrade output. Learners diagnose and fix flawed prompts systematically.

Lesson 1 • Zero-Shot and Few-Shot Prompting

Contrasts zero-shot task framing with few-shot example injection and their effect on output quality. Learners choose the right approach based on task familiarity and data availability.

Lesson 2 • Chain-of-Thought Prompting

Teaches step-by-step reasoning elicitation to improve model accuracy on complex tasks. Learners apply both zero-shot and few-shot chain-of-thought variants.

Lesson 3 • Decomposition and Task Chaining

Breaks complex tasks into sequential subtasks and chains prompt outputs as inputs to subsequent steps. Enables reliable handling of multi-step workflows.

Lesson 4 • Role and Context Injection

Covers injecting domain expertise, situational context, and stakeholder perspectives into prompts. Improves relevance and depth of model responses for specialized tasks.

Lesson 1 • Question Answering and Reasoning Prompts

Structures prompts for open-domain QA, closed-book reasoning, and retrieval-augmented answering. Learners improve factual accuracy and source attribution in answers.

Lesson 2 • Classification and Labeling Prompts

Teaches label definition, class boundary specification, and confidence scoring in classification prompts. Enables consistent, auditable categorization outputs.

Lesson 3 • Information Extraction Prompts

Designs prompts that pull structured entities, relationships, and facts from unstructured text. Learners produce extraction outputs compatible with downstream data pipelines.

Lesson 4 • Text Summarization Prompts

Covers extractive vs. abstractive summarization directives, length constraints, and audience targeting. Learners produce summaries that meet defined quality criteria.

Lesson 1 • Designing Evaluation Test Sets

Covers constructing diverse, representative test cases that expose prompt failure modes. Learners build evaluation sets that reflect real-world input variation.

Lesson 2 • Systematic Prompt Debugging

Teaches root-cause analysis of prompt failures through output inspection and ablation testing. Learners isolate which prompt element causes a given failure mode.

Lesson 3 • Prompt Versioning and Documentation

Introduces version control practices, prompt registries, and change logging for prompt assets. Ensures reproducibility and team-wide prompt governance.

Lesson 4 • Quantitative Prompt Evaluation Metrics

Applies accuracy, F1, BLEU, ROUGE, and semantic similarity metrics to prompt output assessment. Learners select metrics appropriate to each task type.

Lesson 1 • Tool Use and Function-Calling Prompts

Structures prompts that trigger external tool calls, API functions, and code execution within model workflows. Learners write reliable tool-use instructions and parse structured returns.

Lesson 2 • Memory and State Management in Prompts

Manages conversation history, external memory stores, and state summaries within long-running prompt sessions. Learners prevent context overflow while preserving task continuity.

Lesson 3 • Retrieval-Augmented Generation Prompts

Integrates retrieved document chunks into prompts to ground model responses in external knowledge. Learners design retrieval-aware prompt templates that minimize hallucination.

Lesson 4 • Multi-Agent Prompt Architectures

Designs orchestrator-agent and peer-agent prompt systems where multiple models collaborate on tasks. Learners assign roles, manage handoffs, and resolve inter-agent conflicts.

Lesson 1 • Jailbreaks and Adversarial Prompts

Analyzes role-play, hypothetical framing, and encoding tricks used to bypass model safety layers. Learners design prompts and system messages that resist known bypass techniques.

Lesson 2 • Understanding Prompt Injection Attacks

Explains how malicious user inputs override system instructions and manipulate model behavior. Learners recognize injection patterns and apply defensive prompt structures.

Lesson 3 • Organizational AI Use Policies

Translates organizational AI governance policies into prompt-level controls and usage guidelines. Learners align prompt design with data privacy, consent, and acceptable-use requirements.

Lesson 4 • Bias Detection and Mitigation in Prompts

Identifies how prompt framing amplifies demographic, cultural, and confirmation biases in outputs. Learners apply debiasing techniques and audit prompts for fairness.

Lesson 1 • Monitoring Prompt Performance in Production

Sets up logging, drift detection, and quality dashboards to track prompt health over time. Learners identify degradation signals and trigger prompt review cycles proactively.

Lesson 2 • Latency and Cost Optimization

Applies token reduction, caching, and model selection strategies to minimize API cost and response time. Learners balance output quality against operational budget constraints.

Lesson 3 • Prompt Templating and Parameterization

Converts static prompts into dynamic templates with variable slots for runtime data injection. Enables reusable, maintainable prompt assets across multiple use cases.

Lesson 4 • Prompt Governance and Change Management

Establishes approval workflows, rollback procedures, and stakeholder review gates for prompt updates. Ensures safe, auditable changes to production prompt systems.