Generative AI Workflow Automation Course

Lesson 1 • Core Model Architectures Overview

Surveys transformer, diffusion, and multimodal architectures at a conceptual level. Connects architecture choice to practical workflow decisions.

Lesson 2 • Responsible Use and Known Risks

Identifies bias, data privacy exposure, and misuse vectors inherent to generative models. Sets ethical guardrails applied in every subsequent chapter.

Lesson 3 • What Generative AI Actually Does

Covers probabilistic text and media generation, contrasting it with traditional rule-based automation. Establishes vocabulary used throughout the course.

Lesson 1 • Few-Shot and Chain-of-Thought Techniques

Demonstrates how examples and reasoning chains improve accuracy on complex tasks. Directly enables multi-step automation logic covered in later chapters.

Lesson 2 • Advanced Prompt Patterns

Covers meta-prompting, prompt chaining, and self-critique loops for complex tasks. Prepares students for multi-agent orchestration introduced in later chapters.

Lesson 3 • Prompt Versioning and Testing

Introduces systematic A/B testing and version control for prompts as production assets. Establishes quality assurance habits required for reliable automation pipelines.

Lesson 4 • Structured Output Prompting

Teaches prompts that force JSON, Markdown tables, and XML outputs for downstream parsing. Structured outputs are the interface layer between AI and automation tools.

Lesson 1 • Error Handling and Retry Logic

Covers rate limits, timeout errors, and malformed response handling with practical retry strategies. Robust error handling is essential for production-grade automation.

Lesson 2 • Securing and Managing API Credentials

Teaches secret management, environment variables, and credential rotation for AI API keys. Prevents credential exposure in shared codebases and automation platforms.

Lesson 3 • Making Your First AI API Call

Walks through a complete chat completion request using Python and curl. Builds confidence with live API interaction before adding automation logic.

Lesson 4 • REST API Concepts for AI Services

Explains HTTP methods, headers, authentication, and request-response cycles in the context of AI APIs. Provides the technical baseline for all integration work ahead.

Lesson 1 • Deploying and Monitoring Visual Workflows

Covers activation, scheduling, run history, and alerting for production visual workflows. Ensures students can maintain and troubleshoot live automations.

Lesson 2 • Visual Workflow Platform Concepts

Introduces triggers, actions, conditions, and data mapping as universal building blocks across platforms. Establishes a platform-agnostic mental model for all tool-specific work.

Lesson 3 • Integrating External Apps and Services

Connects AI workflows to email, spreadsheets, databases, and messaging tools via pre-built connectors. Demonstrates end-to-end automation across real business systems.

Lesson 4 • Connecting AI Nodes to Workflows

Demonstrates adding AI completion, classification, and summarization nodes to visual flows. Bridges prompt engineering skills to drag-and-drop automation environments.

Lesson 1 • Retrieval Strategies and Reranking

Compares dense, sparse, and hybrid retrieval, then adds reranking for precision. Advanced retrieval strategies significantly improve answer quality in production.

Lesson 2 • Embeddings and Vector Stores

Teaches embedding generation and storage in vector databases for similarity search. Vector stores are the retrieval backbone of every RAG pipeline built in this course.

Lesson 3 • Assembling the Full RAG Pipeline

Integrates ingestion, retrieval, prompt assembly, and generation into a single end-to-end flow. Students deploy a working document Q&A system as the chapter capstone.

Lesson 4 • Document Ingestion and Chunking

Covers loading PDFs, HTML, and plain text, then splitting them into semantically coherent chunks. Chunk quality directly determines retrieval accuracy downstream.

Lesson 1 • Memory Systems for Agents

Covers in-context, external key-value, and episodic memory patterns for stateful agents. Persistent memory enables agents to handle multi-session and long-horizon tasks.

Lesson 2 • Agent Architecture and Planning Loops

Explains the observe-think-act loop and how agents decompose goals into executable steps. Provides the conceptual framework for all agent-building exercises in this chapter.

Lesson 3 • Guardrails and Human-in-the-Loop Design

Integrates approval gates, output validators, and escalation paths into agentic workflows. Guardrails are mandatory for deploying agents in regulated or high-stakes environments.

Lesson 4 • Multi-Agent Orchestration

Teaches supervisor-worker and peer-to-peer agent topologies for parallelizing complex tasks. Multi-agent systems dramatically expand the scope of automatable business processes.

Lesson 1 • Observability and Logging

Implements structured logging, distributed tracing, and dashboards for AI workflow visibility. Observability is the foundation of rapid incident response and continuous improvement.

Lesson 2 • CI/CD for AI Workflow Pipelines

Automates testing, prompt regression checks, and deployment gating in a CI/CD pipeline. Continuous delivery ensures safe, rapid iteration on production AI workflows.

Lesson 3 • Cost Monitoring and Token Optimization

Tracks token consumption, maps costs to workflows, and applies caching to reduce spend. Cost governance prevents budget overruns as AI usage scales across an organization.

Lesson 4 • Containerizing AI Workflow Services

Packages AI workflow code into containers with reproducible dependencies and environment configs. Containerization is the standard deployment unit for modern AI services.

Lesson 1 • Automated and Human Evaluation Methods

Compares LLM-as-judge, reference-based scoring, and human annotation for evaluating outputs. Combining methods balances speed, cost, and reliability of quality signals.

Lesson 2 • Iterative Improvement Cycles

Structures prompt refinement, retrieval tuning, and model swaps as data-driven improvement cycles. Systematic iteration prevents ad hoc changes that degrade production performance.

Lesson 3 • AI Governance Frameworks

Covers risk tiering, model cards, use-case approval workflows, and accountability structures. Governance frameworks align AI automation with organizational and regulatory expectations.

Lesson 4 • Defining Quality Metrics for AI Outputs

Introduces faithfulness, relevance, coherence, and task-completion rate as measurable quality dimensions. Clear metrics make iterative improvement objective and defensible.