Prompt Chaining: A Foundational Technique for AI Development

How AI Learns Artificial Intelligence doesn’t “think” like humans, it learns patterns. Every AI model is trained using thousands (or millions) of data examples; from text, images, or voice. It identifies connections and probabilities, then uses them to make predictions or generate new outputs. That’s why AI improves over time, the more data it sees, the smarter it gets. But here’s the catch: AI doesn’t understand context the way we do. It recognizes patterns, not meaning. So when you interact with AI tools, you’re teaching them, just as they’re assisting you. The future isn’t AI replacing humans, it’s humans guiding AI to work smarter.

Intuitive AI: Can we create technology that actually feels? In 2024, ScienceDirect published an article titled “In Praise of Empathic AI.” Its main idea struck me: artificial intelligence without empathy isn’t really intelligent. We’ve already taught AI to listen, see, translate, and predict - but not to feel. Emotional intelligence remains the last frontier between humans and machines. Researchers are now training new generations of “empathic” models to recognize emotions through tone, micro-expressions, pauses, or even typing rhythm. The goal isn’t just to respond - it’s to connect. But here comes the ethical paradox: If a machine can understand our emotions, could it also manipulate them? Empathic AI could make interactions smoother - in healthcare, education, and customer support. Yet it could also cross the line: when a system knows how we feel, it gains power over our reactions. Before we ask how to build intuitive AI, we should ask why - and where the safe boundary lies. Because intuition without ethics isn’t empathy. It’s control disguised as care. AI is becoming more human. Let’s make sure we stay human too.

If you ask an AI to complete the line “Mary had a little…”, you’ll almost certainly get “lamb.” For most of us we are old enough to understand that she didn’t really, that this is merely a line from a nursery rhyme. We are wise enough to understand that just because it is written, doesn’t mean it is true. For many younger people, thats not the case. Often they believe what they read…..so how many have been wondering for years where Mary’s lamb is whilst she’s in school ? 🤣 Therefore it is vital that we teach them the importance of validating information/data. That’s the quiet risk of our AI era. Tools that sound authoritative can blur the line between fact and fiction, especially for young or inexperienced users. AI doesn’t know, it predicts. And prediction without validation is storytelling dressed as truth. So whether it’s a nursery rhyme or a business decision, the rule stands: ✅ Always verify before you amplify. ✅ Teach people to question, not just consume. ✅ Remember, AI’s strength is speed; ours is judgment. AI may complete the sentence, but it’s our job to complete the thinking.

✅ Always verify before you amplify. ✅ Teach people to question, not just consume. ✅ Remember, AI’s strength is speed; ours is judgment. Chris Loveday Short and sweet, I like it. 1. If you are not the SME in that area, don't amplify. If you are, verify. 2. Critical thinking is key. Whether it's an LLM, the NYT, the WSJ, or your dad, don't just consume. Question. 3. Augmented Intuition. That takes time. Intelligence that is artificial is like Splenda. Ain't the real thing and it will leave a bitter taste. #criticalThinking #BeMoreHuman #HumansAreAwesome #AI #leadership

🎨 Abstraction remains one of the greatest debates in the AI industry — a puzzle still far from being solved. You’ve probably noticed those weird-looking CAPTCHA tests lately. They exist for a reason: to protect against automation and AI exploitation. But what’s the deeper reason behind their evolution? Let’s pause and take a simple example — a painting. Ask yourself: What do I see here? Then ask someone else the same question. Chances are, you’ll both see something completely different. That’s abstraction — the ability to interpret meaning through perspective, emotion, and experience. It’s where human intuition thrives… and where AI still stumbles. So here’s the real question: 👉 How can we teach AI not to treat knowledge as its own fixed judgment — but as a starting point for understanding perspective? Perhaps solving that will be the moment when AI starts to truly “see” — not just “process.” "Fudo" - Stanton MacDonald-Wright (1890–1973) Source: www.virtosuart.com

99% of PMs still confuse AI models with agents. Here's how I explain it to my students: The two are related... but not the same. An AI model is like a specialist. It's trained to perform one specific task. You give it a task → it gives you an output. That's all. It doesn't plan. It doesn't decide. It doesn't act. It just makes a prediction. An AI agent is the orchestrator. It uses models, tools, and memory to achieve a goal. It can: • Decide which model to use • Retrieve information • Execute actions • Learn from feedback In short... Models process data. Agents pursue objectives. This difference changes everything about how we design, evaluate, and ship AI products. Did this help?

LEVERAGING ON AI TO LEARN An AI is a technology system that is designed to simulate human reasoning and provide appropriate results or actions. Leveraging AI systems for learning is highly crucial in this contemporary age because it provides appropriate information and is used as a support for critical thinking, not as a mortal replacement. One of the ways to interact effectively with AI is called Prompting. Prompting in an AI context is the process of inputting explicit information for AI to generate suitable information for the expected result. To harness the power of AI, the following points must be considered: Task:  Be clear and specific with the information you are asking the AI system in order to get a desired outcome. Context:  Provide detailed information to back the task. Exemplar:  Provide a reference object as an example that you want the AI system to emulate. Persona:  You need to let the AI know the kind of character or pattern that matches the context you are trying to generate. Format:  Describe how the information should be structured. Tone:  Let the information match the tone of the context. #ALX_SE #ALX_FE @alx_africa

AI Engineering Unlocked | Day 13 AI's Brain is a Library Not a Newstand. It only knows what on its shelves. AI Time Limit - Training Data and Knowledge Cutoff Why doesn't your AI know about events that happened last week, even though the internet knows? AI models are trained on data up to a specific date, and they don't learn new information after that point—unless they use real-time data tools. Here's a common misconception: people think AI models browse the internet in real-time like search engines do. The reality is quite different. When an AI model is trained, it learns from billions of text examples collected up to a specific date. Let's say a model was trained on data up to January 2025. That becomes its knowledge cutoff. Everything it knows comes from that training data. Anything that happened after January 2025? The model has no idea. This is why you might ask ChatGPT about a recent news event, and it tells you it doesn't have that information. It's not avoiding the question—it genuinely doesn't know. Its training process ended long ago. However, some AI models and applications now integrate real-time data tools. They can search the internet, access current information, and combine it with their training to give you up-to-date answers. But the model itself still doesn't learn from this new information. It's more like a lookup service. Understanding your AI's knowledge cutoff date helps you set realistic expectations and know when to double-check information or use other sources. #AI #AIEngineering #AIUnlocked

AI offers incredible opportunities to increase our efficiency - IF we know how to use it effectively. A client recently told me about one of his younger team members who used AI for a research task - without telling him they used AI. My client received the output and was disappointed. The research did not answer his questions, nor were the references and sources named. After some probing, the employee "confessed" that they had used AI. Unfortunately, not in an effective way. Had the employee known how to write the prompts in a better way, and had the output been to (or even better beyond) the expectations of the manager, this story could have caused that AI becomes common practice for research in this department. What does this tell you about the integration of AI into work routines? It shows that knowing how to use AI properly is just as important as having access to it. Without the right skills, AI can produce more confusion than clarity. Training teams on prompt engineering, critical thinking, and source validation is key. Otherwise, AI becomes a black box — a tool that’s trusted blindly, not used strategically. If we want AI to truly amplify our work, we need to teach people how to wield it. Otherwise, it’s just another shiny object, not a game changer.

AI is making us look smarter - but not wiser TL;DR: Turns out, the more you know about AI, the more likely you are to overestimate how well you’re using it. Ouch. A new study shows AI users - especially the “AI literate” ones - are bad at judging their own performance. People using ChatGPT scored better on logic tasks, but thought they did way better than they actually did. In addition, the more confident the user, the less likely they were to double-check or reflect. Researchers call this “cognitive offloading” - trusting AI too much, too fast. My take on it: This is happening in all fields of knowledge, but usually, the Dunning-Kruger cognitive bias *decreases* as knowledge reaches a certain level. AI tools are powerful, but they’re not magic. And confidence without reflection is a dangerous mix. Don't deploy AI capabilities without a strong focus on AI literacy emphasizing things like critical thinking, human oversight, and ethical use. Because AI transformation isn’t just about tools - most of it is about how people use them. The real challenge isn’t teaching people how to use AI. It’s helping them stay curious, humble, and aware while doing it. Image credit: Futurism