Applied AI

Applied AI · Details

Workflows, Automation, and Cognitive Engineering

Applied AI is a twenty-hour course for people and teams who want to move beyond an initial understanding of Artificial Intelligence and learn how to apply generative tools in real contexts of work, study, creation, communication, automation, and prototyping. The course builds on the conceptual foundation of AI Essentials, while expanding the approach through additional practice, guided exercises, demonstrations, workflow construction, and the development of an applied project. Its purpose is to enable participants to understand the foundations of contemporary AI, distinguish among ecosystems, assistants, knowledge bases, RAG systems, and agents, produce generative content in multiple formats, and begin designing useful AI solutions even without prior technical training.

Module 1 — Foundations of Applied AI

Class 1 — Foundations of Contemporary AI

This module establishes the conceptual foundation of the course. It defines Artificial Intelligence, situates the field’s development historically, and presents the most important concepts for understanding how current systems work. It also introduces the operation of language models and the main limitations involved in their use, creating the basis participants need to use AI tools with greater understanding and less noise.

  • What Artificial Intelligence is
  • A brief history of AI
  • Earlier and current forms of AI
  • Fundamental concepts: model, token, prompt, and context
  • How language models work
  • Limitations, errors, and hallucinations

Module 1 — Foundations of Applied AI

Class 2 — Ecosystems, Assistants, Knowledge Bases, and Agents

This class presents the main environments in which AI is used today and distinguishes several types of systems that are often mixed together in everyday conversations. Its focus is to explain clearly what ecosystems, assistants, knowledge bases, and agents are, showing where each appears and how these structures are used by people and organizations.

  • Main AI environments and companies
  • What are assistants?
  • What are knowledge bases?
  • Knowledge bases with information search and retrieval
  • What are agents?
  • Practical differences among assistants, knowledge bases, and agents

Module 2 — Applied Generative Production

Class 3 — Generative Text Automation

This class presents generative text production as a process that can be structured, directed, and automated with the support of assistants, agents, and knowledge bases. Its focus is to show how AI can support the creation of articles, scripts, essays, short stories, books, educational materials, and professional documents without sacrificing human direction, quality criteria, review, and grounding in references.

  • Generative Assistants
  • Generative Agents
  • Generative Creation Techniques
  • Knowledge Bases as grounding
  • Simplified Workflow
  • Precision Agents for Creation

Module 2 — Applied Generative Production

Class 4 — Generative Audiovisual Automation

This class presents audiovisual production supported by AI, exploring audio, narration, images, lip sync, video, and alternative media. Its focus is to show how multimodal prompts, visual references, conceptual anchors, and iterative processes make it possible to create more consistent work. The class also discusses the limits of language when directing visual and sound systems, as well as the ethical questions involved.

  • The Limits of Language
  • Anchoring References
  • Human Judgment and the Iterative Process
  • Multimodal Prompts
  • The New Prompt Engineering
  • Ethical Questions

Module 3 — AI Systems Engineering

Class 5 — Context Engineering and the New Prompt Engineering

This class presents Context Engineering as the natural evolution of Prompt Engineering. Instead of relying only on prose commands, participants learn to organize information, topics, criteria, examples, documents, and layers of context to obtain more precise responses. Its focus is to show how topical organization, semantic structuring, and information management reduce fluctuations and make agents more consistent.

  • What is Context Engineering?
  • Fluctuations versus Hallucinations
  • Information Management
  • Layers of Context
  • Conversational Agents
  • Automation Agents

Module 3 — AI Systems Engineering

Class 6 — Intention Engineering and Precision Agents

This class presents Intention Engineering as the practice of transforming human objectives into clear instructions, methods, and operational schemas for AI systems. Its focus is to show how vague intentions can be converted into precise tasks with criteria, constraints, and procedures. The class also introduces precision agents, designed to reduce loose responses, limit fluctuations, and operate with greater methodological control.

  • What is Cognitive Engineering?
  • What is Intention Engineering?
  • Language, Prose, and Schemas
  • Instructions and Methods
  • Method Builders
  • Precision Agents

Module 4 — Applied Workflows and Automation

Class 7 — AI Workflows

This class presents the use of AI in workflows, showing how to combine tools, assistants, agents, and procedures into coherent processes. Its focus is to move beyond the isolated use of a tool and understand how different stages can be connected for research, writing, audiovisual production, analysis, review, and delivery. Participants learn to design simple workflows applicable to real problems.

  • What are workflows?
  • Assistants, Agents, and Procedures
  • Text Creation Workflow
  • Audiovisual Workflow
  • Creating Workflows
  • Proposed Generative Workflow

Module 4 — Applied Workflows and Automation

Class 8 — Generative Automation

This class presents generative automation as a way of connecting AI to recurring work processes. Participants become familiar with tools such as MindStudio, Zapier, FlowWise, and n8n, understanding how triggers, actions, conditions, APIs, endpoints, and integrations can structure automated workflows. Its focus is to show how to think in flows, design useful automation, and avoid errors at scale caused by a lack of human review.

  • What is automation?
  • Building Automated Workflows
  • Work Tools
  • Thinking in Flows
  • Designing Workflows
  • APIs, Endpoints, MCP, and Other Creatures

Module 5 — Applied Cognitive Engineering

Class 9 — Vibe Coding and Vibe Weaving

This class presents the use of AI to program, prototype, and weave digital systems. Vibe coding is approached as AI-assisted generative programming, while vibe weaving broadens the perspective to the composition of systems, libraries, APIs, workflows, interfaces, and personalized agents. Its focus is to show how nontechnical people can participate in the development of solutions without losing sight of architecture, review, and responsibility.

  • What is Generative Programming?
  • What is Generative Systems Analysis?
  • Libraries, Git Repositories, and APIs
  • Developing a Program
  • Developing a Project
  • Personalized Agents

Module 5 — Applied Cognitive Engineering

Class 10 — Agent Engineering and Loops

This class presents Agent Engineering and Loops as the most advanced stage of Applied AI. Its focus is to show how agents can be designed with roles, tools, instructions, memory, methods, and supervision criteria, and how loops make it possible to create cycles of execution, verification, review, and continuity. The class concludes the course by connecting automation, agents, and recurring loops in simple cognitive systems.

  • What is Agent Engineering?
  • What is Loop Engineering?
  • Automation and Agent Engineering
  • Automation and Loops
  • Developing Agents
  • Developing Loops

Total duration of the complete program: 20 hours.
The program is organized into 5 modules, comprising 10 two-hour classes.