Prompt Engineering + Real Practices
AI has deeply transformed how we work, learn, and solve problems. My goal is not just to teach concepts but to guide you in changing your way of thinking. This session mixes knowledge from the Andrew Ng & Isa Fulford Prompt Engineering Course with my own daily experiences of learning and building with AI.
Everything in prompt engineering is built on two powerful rules:
AI is like a smart assistant who wants to help but easily gets confused if you’re vague. Writing clearly is like giving a friend exact directions: *not just “Go over there”*, but “Walk 2 blocks north, then turn right at the café.” It’s not about making prompts short, it’s about making them unambiguous.
Delimiters are like fences that clearly mark what belongs where. They reduce confusion by separating instructions from data.
Example: Ask AI to summarize text inside """ so it knows exactly which part to work on.
Instead of a vague “Tell me about…” → ask AI to put its response into a format you can use directly. This saves time and avoids messy answers.
Before asking the model to solve something, make sure it has the necessary context. Don’t assume it “knows” everything. If information is missing, the model may fill gaps with fantasy. Always check: “Does AI have what it needs to solve this?”
Don’t just explain the rules—show examples. When you show AI how you want the answer to look with 2‑3 samples, it usually follows that exact style in its future responses.
Example: Give it a “Q → A” pair, then ask a new question.
AI struggles when you rush it for a final answer on a hard task. Instead, let it “talk through the problem” by prompting it to walk step by step. This is like asking a friend in math class: *“Don’t just tell me the answer, show your working.”*
Sometimes AI makes things up—confidently. It can output content that sounds plausible but is factually wrong. These are called hallucinations.
Think of it like a student bluffing in an exam: speaking smoothly, but wrong.
Prompting is not one‑and‑done. Like scientific experiments, you try → review → refine → repeat.
AI shows its strength in three core use cases: Summarizing, Transforming, Expanding. Each one helps us think more clearly and work more efficiently.
Instead of reading long complex text, AI can condense information to key insights so you see the bigger picture quickly.
Example: “Summarize this report into 5 bullet points focusing on risks.”
AI is powerful at converting knowledge from one form into another, bridging gaps between languages, formats, and complexity levels.
Example: “Explain this Java code in a way a beginner Python learner would understand.”
AI is great at enriching short text into detailed context. Expanding is not about adding fluff—it’s about creating meaning, structure, and flow.
Example: “Take these 3 bullet points and turn them into a professional email for my team.”
Beyond the course lessons, my personal practices and experiences show how AI truly becomes a partner in learning and work.
Many times my head is full of vague plans. With AI, I convert raw thoughts into step-by-step systems. This turns imagination into practical action.
Example: I explain a workflow in plain text → AI helps me draft pseudocode or diagrams → I refine until it becomes working software.
When learning new concepts, I start from what I know and let AI fill in the gaps gradually. I don’t just ask for definitions, I discuss with AI like a mentor.
I talk to AI like a real conversation partner. It has the breadth of global knowledge, my job is to keep asking until I understand. If I don’t get it, I ask for simpler explanation until it clicks.
I often dive straight into tools and let AI fill knowledge gaps. Instead of long tutorials, I use AI to get immediate clarity, then build as I learn.
I rely heavily on AI to draft study notes, structure documents, and make them share-ready. This applies both to personal learning notes and public docs for others.
On Ubuntu, I often need quick and efficient help for everyday tasks. AI acts as my command-line buddy.
I automate repetitive web tasks using tools like Selenium with help from AI. This saves huge time and effort.
AI is my coding assistant across both familiar and new problems. Sometimes I need quick code I know but don’t want to retype; other times, I want help on bugs I’ve never seen.
Using AI is not about magic. It’s about learning how to talk with it clearly and work with it iteratively. Treat AI as a skilled partner: when you guide it step by step, it helps transform vague thoughts into real outcomes.