Context Engineering

Foundations, Language, Information Management, and Agent Architecture

Context Engineering is a complete program on how to structure information, intention, and memory to improve the performance of Generative Artificial Intelligence. The course presents the conceptual foundations of contemporary AI, explains how Natural Language Models work, and shows why context has become the principal axis of quality, coherence, and control in generative systems. Throughout the program, participants learn to understand fluctuations, design more robust prompts, organize knowledge bases, plan conversational agents, distinguish automation agents, and apply Intention Engineering methods. Its purpose is to develop a practical and strategic perspective for professionals who want to use AI more precisely, productively, and safely in real work environments.

Part I

AI and LLM Foundations and Initial Uses

This first part establishes the course’s common vocabulary. Its objective is to ensure that all participants understand the foundations of Artificial Intelligence, the essential concepts used throughout the program, and the operational logic of Natural Language Models before advancing to Prompt Engineering and Context Engineering.

Module 1 — Introduction

A brief journey through Context Engineering and the concepts surrounding it. The module presents the course’s central thesis: better AI results depend less on magic phrases and more on the deliberate construction of a coherent contextual field.

  • What Context Engineering is
  • Why context became central to generative AI
  • The difference among prompt, context, and instruction
  • Context as a structure of meaning
  • Limits of the intuitive use of generative tools
  • Overview of the program and its three parts

Module 2 — Glossary

An expanded glossary for anyone who needs a general overview of the terms used throughout the course. The module organizes the main technical and semantic concepts to reduce noise and establish terminological precision.

  • Model, prompt, context, and token
  • Context window and memory
  • Inference, training, and fine-tuning
  • Hallucination, fluctuation, and contextual error
  • Agents, assistants, and automation
  • RAG, knowledge bases, and semantic retrieval

Module 3 — Artificial Intelligence

The history of AI, so that students have a clear idea of how we arrived here and where we have actually arrived. The module situates generative AI within a broader trajectory of research, cycles of expectation, and technological change.

  • A brief history of Artificial Intelligence
  • Symbolic, statistical, and generative AI
  • Machine learning and neural networks
  • The leap of foundation models
  • AI as tool, system, and ecosystem
  • Current limitations and realistic expectations

Module 4 — LLMs: What They Are and How They Work

What the AI architecture called an LLM is, and what we actually know about how it works and operates. The module presents the operation of Natural Language Models without mystification, but with enough care to avoid misleading simplifications.

  • What Large Language Models are
  • How models process language
  • Tokens, embeddings, and semantic relationships
  • Probabilistic prediction and text generation
  • Active context and situated responses
  • What we know and still do not know about LLMs

Module 5 — Demonstrations and Use Cases

The materialization of what has been discussed so far through tests with Language Models. Through practical examples, the module shows how the ideas presented appear in the everyday use of generative tools.

  • Simple tests with language models
  • Comparison of responses with and without context
  • Examples of progressive prompt improvement
  • Use of instructions, examples, and constraints
  • Identification of limitations and unexpected behaviors
  • Use cases in communication, analysis, and productivity
Part II

Prompts, Language, and Fluctuations

The second part explores the transition from traditional Prompt Engineering to a broader practice guided by language, context, and the diagnosis of fluctuations. Participants learn to recognize how linguistic formulation, semantic framing, and the available context shape model behavior.

Module 6 — The New Prompt Engineering

A new perspective on Prompt Engineering after the arrival of context-sensitive Natural Language Models. The module repositions the prompt as part of a broader contextual architecture rather than an isolated formula.

  • Limits of conventional Prompt Engineering
  • The prompt as a context operator
  • Instruction, role, task, and quality criterion
  • Examples, constraints, and output format
  • Reusable and situational prompts
  • Transition from prompts to contextual design

Module 7 — Demonstrations and Use Cases

The manifestation of the New Prompt Engineering and its differences from the conventional method of working with LLMs. The module compares traditional and contextual approaches to demonstrate gains in precision, stability, and usefulness.

  • Comparison between weak and structured prompts
  • Rewriting poorly formulated tasks
  • Using role context and objective context
  • Explicit criteria for evaluating responses
  • Transforming prompts into small procedures
  • Use cases in analysis, writing, and decision-making

Module 8 — Language: One Step Back…

Understanding Prompt Engineering and Context Engineering requires an understanding of Language. The module presents language as the operational medium of LLMs and as the structure that organizes meaning, ambiguity, intention, and interpretation.

  • Language as the operational medium of LLMs
  • Ambiguity, polysemy, and framing
  • Meaning, reference, and discursive context
  • How instructions alter interpretation
  • The role of narrative in response coherence
  • The relationship among language, intention, and results

Module 9 — Fluctuations

Fluctuation is a more accurate term than hallucination, which assigns blame more readily than it solves the problem. The module explains why inconsistent responses often arise from gaps, excesses, or distortions in the available context.

  • Why replace “hallucination” with “fluctuation”
  • Fluctuations caused by insufficient context
  • Fluctuations caused by contradictory context
  • Fluctuations caused by excessive complexity
  • Toxic contexts and false evidence
  • Mitigation and diagnostic strategies

Module 10 — Demonstration and Use Cases

The impact of understanding Language, Fluctuations, and Toxic Contexts. The module demonstrates how small contextual changes can profoundly alter responses and how to diagnose what is missing or excessive.

  • Demonstrations of fluctuation caused by insufficient context
  • Demonstrations of fluctuation caused by biased context
  • Comparison between prompt correction and context correction
  • Diagnosis of false premises in a request
  • Using clarifying questions as a control mechanism
  • Use cases in documents, service, and critical analysis
Part III

Information Management, Agents, and Intention Engineering

The third part brings Context Engineering into the construction of systems and operations. The focus shifts to information organization, the creation of conversational agents, the distinction between conversational and automation agents, and the formulation of intention as an operational component of generative systems.

Module 11 — Information Management

How to undertake Information Management in support of Context Engineering. The module presents curation, provisioning, and continuous supply as fundamental processes for sustaining coherence in AI systems.

  • Information Management applied to generative AI
  • Curation of content and sources
  • Context and infrastructure provisioning
  • Continuous information supply
  • Organization of knowledge bases
  • Traceability, updating, and quality control

Module 12 — Conversational Agents

How GPT Agents and Gem Agents in OpenAI and Google tools work, and what they actually are. The module explains conversational agents as contextual constructs sustained by instructions, knowledge, and forms of interaction.

  • What a conversational agent is
  • The difference between a standard chatbot and a specialized agent
  • Custom instructions and operational identity
  • Knowledge bases and supporting files
  • Tools, actions, and integrations
  • Good practices for conversational agent design

Module 13 — Automation Agents

How Automation Agents work and how they differ from Conversational Agents. The module presents automation as action guided by tools, workflows, states, and integration with external systems.

  • What characterizes an automation agent
  • The difference between conversing and executing
  • Workflows, tasks, tools, and states
  • Integrations with APIs and external systems
  • Operational risks and control points
  • When to use automation and when to use conversation

Module 14 — Building Agents

Agent production today can benefit greatly from new Context Engineering techniques. The module brings together the preceding concepts in a practical process for designing, specifying, testing, and improving agents.

  • Defining the agent’s purpose and scope
  • Designing identity, knowledge, and operation
  • Building instructions and response criteria
  • Organizing files and knowledge bases
  • Testing, validation, and contextual refinement
  • Agent documentation and maintenance

Module 15 — Intention Engineering

To clarify intention within a context, Intention Engineering uses Operational Context. The module presents intention as a layer that guides action, criteria, decisions, and execution in generative systems and agents.

  • What Intention Engineering is
  • Intention as an operator of action
  • The relationship among objective, task, and criterion
  • Operational Context and decision-making
  • How to make intention explicit in prompts and agents
  • Applications in strategy, automation, and governance

Engagement Formats

The program is available in three formats, according to the desired level of depth and the participating team’s availability.

Executive Workshop

10 hours

Compact Track

20 hours

Complete Program

60 hours

Total duration of the complete program: 60 hours.
The program is organized into 15 modules, with 2 two-hour classes per module.