
A short introduction to what this course covers and how it's structured, so you know exactly what to expect.
Understand why clever prompt wording alone can't fix inconsistent AI answers.
Learn the core definition of context engineering and the role it plays in every AI response.
See exactly how these two skills differ and where each one actually applies.
Get a clear overview of the path this course will take you through, section by section.
Get oriented in the two AI tools you'll be practicing in throughout the course.
Watch the exact same request produce two very different answers, depending on the context behind it.
Practice opening up an AI's memory settings and seeing exactly what it has saved about you.
Practice reviewing and correcting an AI's memory so it only holds facts that are actually true and useful.
Set up a personal workbook or template to track your prompts and progress throughout the course.
Get oriented in Udemy's player, so you know how to navigate lectures, resources, and progress tracking.
Learn how Udemy's built-in AI assistant works, so you can use it to support your learning as you go.
Get a plain-language explanation of retrieval-augmented generation, without the technical jargon.
Learn which of these three options actually fits the task in front of you.
Practice uploading your own documents so an AI can reference them directly in its answers.
Recognize the most frequent ways people misuse AI tools, and the simple fixes for each one.
Learn a clear, repeatable framework for structuring any prompt you write.
Practice building a prompt from scratch using role, context, and task as your foundation.
Practice setting persistent instructions so an AI behaves consistently across every conversation.
Practice combining a persistent instruction layer with a task-specific prompt in a single request.
Understand what it actually means for an AI's answer to be "grounded" in real information.
Learn how to judge which tasks are actually a good fit for handing over to AI.
Learn a simple way to trace a bad AI answer back to its actual root cause.
Practice recognizing when a long conversation has overloaded the AI's context, and fixing it on the spot.
Practice restarting a conversation the right way, without losing the details that still matter.
Learn how to turn a one-off prompt into a process you can reuse reliably.
Practice breaking a long document into pieces so an AI can process all of it properly.
Learn how to verify whether an AI's answer is actually grounded in your source material.
Practice testing your knowledge base to tell a grounded answer apart from a confident guess.
Review a simple checklist for judging whether an AI's answer is actually reliable.
Practice combining every technique from the course into a single real-world request.
Get clear instructions for the final project that ties everything you've learned together.
Get direction on what to explore next once you've completed this course.
A short closing message to send you off and encourage you to put what you've learned into practice.
A quick, simple guide to sharing your feedback and rating the course once you've finished it.
Learn exactly where to find and download your certificate of completion once you're done.
This course contains the use of artificial intelligence.
Most people trying to get better results from ChatGPT or Claude focus entirely on writing a better prompt. It helps a little, but the answers still feel inconsistent — sometimes sharp and specific, other times vague or just wrong. The real issue usually isn't the prompt itself. It's everything around it: what the AI remembers, what it's actually grounded in, and how much of your intent it can even see at once.
Learn Context Engineering To Get Consistently Better Results From ChatGPT And Claude
This course teaches the skill that sits underneath good prompting — managing the context an AI is working from, on purpose, instead of leaving it to chance. You'll start with the fundamentals of what context actually is and why it shapes every single response you get back.
From there, you'll work through how AI memory actually functions across sessions, how to audit and correct what's being remembered about you, and how to hand an AI your own documents so its answers are grounded in real information instead of guesswork. You'll also learn a clear framework for structuring prompts, how to set up custom instructions that stay consistent across every conversation, and how to combine both of these techniques into a single, reliable approach.
A large part of this course is dedicated to diagnosis — learning to recognize why an AI gave you a bad answer, whether that's context overload, an ungrounded knowledge base, or a document that was too long to process properly, and exactly what to do about each one.
In this course, you'll learn how to:
Explain what context engineering is and why it matters more than prompt wording alone
Manage and audit AI memory across ChatGPT and Claude
Build a knowledge base and test whether answers are actually grounded in it
Apply a clear framework for structuring reliable prompts
Set up custom instructions for consistent AI behavior
Diagnose why an AI gave a bad answer and fix the underlying context problem
Break long documents into chunks an AI can actually process well
Combine all of these techniques into one repeatable AI workflow
Every technique in this course is followed by a short, hands-on practice video inside ChatGPT or Claude, so you're applying each idea immediately rather than just watching it explained.
By the end of this course, you'll have a completely different relationship with these tools. Instead of rewriting the same prompt five times and hoping for a better result, you'll know exactly what's shaping the answer you're getting — and exactly what to change to get the one you actually need.