
Answering common questions and misconceptions about Gen AI
How to protect your data and privacy when using a LLM
The most common prompt pattern is used in the majority of chats , but has flaws !
This prompt pattern is when you realize you can shape how AI responds.
If you used online customer feed back bots (Retail, Service, Support etc. ) then it's like this prompt pattern was used.
In this pattern, you’re not just prompting, you’re deliberately shaping how the AI responds.
This is a must have when working with AI !
We’re going to build something simple, but persistent.
No need to rewrite the same long prompt every time.
Once classification is consistent, the next step is response.
How a GPT becomes a tool
For this GPT, context comes from the uploaded policy document and our instructions.
Configure the Policy Support GPT step by step
Testing the efficacy and reliability of the GPT
A high level overview of how UI based agents turn casual chat into structured, repeatable workflows
They are not perfect though, as I will explain!
See how an agent transforms ambiguous workplace communication into a consistent, reliable format
Watch how an agent applies a predefined analytical structure to transform retail sales data into business insights
In this lecture, we take the structured GPT assistant you’ve already built and turn it into a workflow based AI bot using Agent Builder
Adding nodes that prepare the bot for the primary agent
Adding the primary agent with the instructions and knowledge to handle user policy queries
Testing the veracity of the bot in a chat
Chatting with the bot from a browser
How well does it do when asked asked to perform computations
Generative AI is everywhere – but I find that most people are still using it like a basic chat or search tool.
I want to show you how to change that.
In this advanced-basics practical introduction, you’ll learn how to move from open-ended chat to structured, controlled AI systems. We start with structured prompting — designing behaviour instead of repeating instructions and hoping for consistent results.
From there, you’ll see how Projects and GPTs help persist that behaviour, before moving into UI Agents and Agent Builder, where structure becomes more deliberate and repeatable.
Along the way, you’ll build real examples, including a policy-based assistant grounded in a real document. You’ll see how AI can follow defined rules, apply constraints, and produce consistent, professional outputs that are clearer, more reliable, and easier to verify.
This is not a coding course. It’s about understanding how modern AI tools actually work in practice, and how to design them intentionally so your outputs are safer, more structured, and fit for real workplace use.
If you want to go beyond “ask and hope” prompting and begin building structured AI systems you can confidently rely on, this course provides a clear, step-by-step foundation you can apply immediately.
No hype. No jargon. Just practical, usable skills.