Fundamentals of Generative AI Context Engineering

FROM HYPED GEN AI TO PRODUCTION-GRADE GEN AI

Gen AI has taken over every conversation in the workplace. Everyone has an opinion on it, most companies are adopting it in some form... but very few people can tell you, concretely, what it's actually good at, where it breaks down, and how to build something reliable on top of it.

And so many of these issues relate to the context. Bad information, the context window sliding, insufficient information, and other context-related issues are the true causes of hallucinations (and just bad LLM answers!) at the end of the day.

In this course, we will explore the key problems that may occur with an LLM's context - and, more than anything else, how to address them.

THE TWO MODULES

• Gen AI Literacy: We'll start from first principles: what generative AI is, how it's being adopted (and misused!), the three tiers of Gen AI usefulness, how it's reshaping the nature of work itself, the key technical building blocks of LLMs, and the common pain points organizations hit when rolling it out;

• The Context Layer: Then, we'll go deep into building reliable Gen AI systems, across six lessons: managing the context window itself, deciding when to escalate or clarify instead of guessing, preventing error propagation across multi-agent systems, handling context in large codebases, designing human review that actually catches what matters, and preserving data provenance to resolve conflicts and communicate uncertainty honestly;

LET ME TELL YOU... EVERYTHING

Transparency matters to me, so here's the full megalist of everything covered in this course. No surprises, no filler:

• You will learn what generative AI is and how it differs from discriminative AI;

• You will learn what foundation models are and why they're general-purpose;

• You will learn about the UVAS (the Uncanny Valley of Automated Self) and what it means for your own work;

• You will learn why local productivity gains from Gen AI don't always translate into global gains;

• You will learn the three tiers of Gen AI usefulness, and concrete examples of tasks in each tier;

• You will learn what Gen AI is genuinely great at: rewriting, structuring, drafting, ideation, and basic planning;

• You will learn where Gen AI is dangerous: high-stakes decisions, deep factual retrieval, and hidden-probability tasks;

• You will learn the difference between Gen AI hype and its true, measurable capability;

• You will learn how Gen AI shifts work from execution to automation, and from working to workflow design;

• You will learn the three Gen AI work modes: co-creation, validation, and orchestration;

• You will learn the technical characteristics that define LLM quality: parameters and context window size;

• You will learn how tokenization and embeddings actually work under the hood;

• You will learn why hallucinations happen, and the difference between fabrication, confabulation, and overprecision;

• You will learn about fine-tuning, LoRAs, and Retrieval-Augmented Generation (RAG);

• You will learn how the attention mechanism affects the quality of chained or multitasked prompts;

• You will learn the seven common pain points organizations hit when adopting Gen AI, from unrealistic expectations to stochastic degradation;

• You will learn how LLM context windows work, and why APIs such as the Claude API are stateless;

• You will learn the difference between conversation history, system prompts, retrieved documents, tool outputs, and structured memory;

• You will learn the tradeoffs of larger contexts: cost, latency, and attention dilution;

• You will learn how prompt caching reduces cost without acting as a memory mechanism;

• You will learn the Lost-in-the-Middle effect and how to mitigate it with placement, headers, and repetition;

• You will learn the Facts Block pattern for protecting decision-critical information;

• You will learn a three-tier model for what context deserves preservation versus compression;

• You will learn why progressive summarization is lossy, and how to avoid compounding those losses;

• You will learn how to trim oversized tool outputs before they pollute the context window;

• You will learn the three true escalation triggers: explicit user requests, policy ambiguity, and inability to make progress;

• You will learn why sentiment and self-reported confidence are unreliable escalation signals;

• You will learn how to handle ambiguous or conflicting tool results through clarification rather than guessing;

• You will learn how to classify errors (transient, validation, permission, business-rule) for smarter recovery;

• You will learn the principle of local error recovery, and when to escalate to a coordinator instead;

• You will learn how to build structured error responses that include category, retryability, and suggested action;

• You will learn the difference between an access failure and an empty result, and why conflating them is dangerous;

• You will learn the principles of graceful degradation and preserving partial results under failure;

• You will learn why large codebases exceed practical context limits, and how context degrades during long investigations;

• You will learn the Scratchpad Pattern for preserving discoveries, decisions, and open questions during exploration;

• You will learn how to delegate to specialized subagents for parallel, focused investigation;

• You will learn phase-based exploration, and when to use context summarization, such as Claude Code's /compact command, strategically;

• You will learn how to design stratified sampling for human review based on risk, not random chance;

• You will learn how to calibrate model confidence against real-world performance data;

• You will learn how to build a continuous improvement loop from human review feedback;

• You will learn what data provenance is and why claims without sources are just opinions;

• You will learn claim-to-source mapping, temporal metadata, and how to handle conflicting sources transparently;

MY INVITATION TO YOU

This course comes with Udemy's standard 30-day money-back guarantee, so there's no risk in giving it a try, and I'd encourage you to check out the free preview videos first to get a feel for the teaching style.

If you're ready to move past the hype and actually understand how to work with Gen AI and build reliable Claude-based systems, I'd love to have you in the course.

See you on the inside!